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MVLevelDB+: Meeting Relative Consistency Requirements of Temporal Queries in Sensor Stream Databases

ACM Transactions on Embedded Computing Systems 2024年第1期24卷 1-26页

作者： Lam, Kam-Yiu Zhao, Xiaofei Zhu, Chunjiang Kuo, Tei-Wei Department of Computer Science City University of Hong Kong Kowloon Hong Kong Department of Computer Science The University of North Carolina at Greensboro GreensboroNC United States Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Ensuring relative consistency in executing temporal queries to access real-time sensor data streams maintained in a database is a challenging problem, particularly when data transmission delays are lengthy and highly variable. Due to the unordered arrivals of sensor data, the databases may contain numerous open data versions (ODVs) with undefined validity intervals. Accessing ODVs may violate the relative consistency requirements of temporal queries, resulting in incorrect results. Although the Re-execution with Every Update (REU) method can resolve this issue, it may introduce heavy re-execution costs and significant delays in query completion. In this article, we study the problem of retrieving data items with temporal consistency requirements in a multi-data-stream database. To balance response time and meet the relative consistency requirements of queries, we introduce an enhanced REU mechanism called Re-Execution with Deadline (RED). Moreover, we propose a novel optional mechanism called Backward Execution Option (BEO) for temporal queries to achieve relative consistency in their execution with quick results by relaxing the data freshness constraints. By combining RED with BEO, we formulate the Repeated BEO (RBEO) to further reduce the query response time. We extend the timestamped key-value store MVLevelDB into MVLevelDB+ to implement the proposed mechanisms. To reduce the query re-execution cost as required in RED and REU, we designed the Query Pool with Execution State (QpES) mechanism to achieve relative consistency in query execution with lower checking overhead and only one re-execution. We conducted extensive evaluation experiments on MVLevelDB+ using benchmark programs to illustrate their performance characteristics on handling temporal queries in the modeled IoT system. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Spatio-temporal data

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Increasing Emergency Response Time: A Smart GPS and IoT-Based Approach for Ambulance Optimization 2

Increasing Emergency Response Time: A Smart GPS and IoT-Base...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Prajwal, V. Rahul, I.V. Sai Adithya, A. Chayadevi, M.L. Computer Science and Engineering BNMIT Bangalore India HOD Computer Science and Engineering BNMIT Bangalore India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

This paper proposes a novel approach to enhance ambulance response times and streamline patient transportation during emergencies by leveraging a smart GPS-based system integrated with Internet of Things (IoT) technology at traffic signals. Traditional methods, relying on camera detection or manual traffic clearance, often lead to delays in granting priority to ambulances. In contrast, our system pre-empts traffic signals in real-time, ensuring swift passage for ambulances by communicating with IoT devices installed at traffic control boxes. The proposed system optimizes ambulance routes using real time traffic data and machine learning algorithms, dynamically adjusting paths to avoid congestion. Components include a smart GPS navigation system, IoT devices at traffic signals, robust communication protocols, and centralized control systems. In summary what makes this design effective is its ability to prioritize clearing intersections ahead of emergency vehicles thus reducing time spent on responding to medical calls while at same time giving hope for better outcomes among patients. © 2024 IEEE.

关键词： Traffic signals

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ComStreamClust: a Communicative Multi-Agent Approach to Text Clustering in Streaming Data

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Annals of Data science 2023年第6期10卷 1583-1605页

作者： Najafi, Ali Gholipour-Shilabin, Araz Dehkharghani, Rahim Mohammadpur-Fard, Ali Asgari-Chenaghlu, Meysam Department of Computer Science and Engineering Sabanci University Istanbul Turkey Department of Computer Engineering University of Tabriz Tabriz Iran Computer Engineering Department Isik University Istanbul Turkey Department of Computer Engineering Sharif University of Technology Tehran Iran

Topic detection is the task of determining and tracking hot topics in social media. Twitter is arguably the most popular platform for people to share their ideas with others about different issues. One such prevalent issue is the COVID-19 pandemic. Detecting and tracking topics on these kinds of issues would help governments and healthcare companies deal with this phenomenon. In this paper, we propose a novel, multi-agent, communicative clustering approach, so-called ComStreamClust for clustering sub-topics inside a broader topic, e.g., the COVID-19 and the FA CUP. The proposed approach is parallelizable, and can simultaneously handle several data-point. The LaBSE sentence embedding is used to measure the semantic similarity between two tweets. ComStreamClust has been evaluated by several metrics such as keyword precision, keyword recall, and topic recall. Based on topic recall on different number of keywords, ComStreamClust obtains superior results when compared to the existing methods. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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Classification of autism severity levels using facial features and eye gaze patterns

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Multimedia Tools and Applications 2025年第16期84卷 15853-15866页

作者： Jiji, G. Wiselin Department of Computer Science and Engineering Dr. Sivanthi Aditanar College of Engineering Tamil Nadu Thiruchendur India

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with varying degrees of severity. Early diagnosis and classification of autism severity are crucial for personalized intervention and support. This study proposes a novel approach using image processing techniques to analyze facial features and eye gaze patterns to differentiate between mild, moderate, and severe autism cases. A comprehensive dataset comprising facial videos and images of individuals aged 2 to 12 years with ASD was collected, along with normally developing children. Features such as facial asymmetry is calculated using SIFT feature, eye size, inter-eye distance, and eye openness were extracted for both groups using canny and adaptive thresholding techniques. Support Vector Machine (SVM) classifiers were employed to classify autism cases into three severity levels. Results revealed that eye gaze patterns were significantly lower for autism cases and higher for severe cases. Facial asymmetry was higher for autism cases, showing greater deviations from mild to severe cases. Severe autism cases exhibited extreme stiffness in facial muscle control, leading to the absence of facial expressions. Additionally, inter-eye distance increased, and eye openness decreased for severe autism cases. The proposed method demonstrates promising discrimination performance, as evidenced by high accuracy of 98.70 and sensitivity and specificity of 100 and 97.30. This research contributes valuable insights into the potential use of image processing techniques for early autism diagnosis and effective severity classification. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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A Construction of Object Detection Model for Acute Myeloid Leukemia

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Intelligent Automation & Soft Computing 2023年第4期36卷 543-560页

作者： K.Venkatesh S.Pasupathy S.P.Raja Department of Computer Science and Engineering Annamalai UniversityChidambaram608002India School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India

The evolution of bone marrow morphology is necessary in Acute Mye-loid Leukemia(AML)*** takes an enormous number of times to ana-lyze with the standardization and inter-observer ***,we proposed a novel AML detection model using a Deep Convolutional Neural Network(D-CNN).The proposed Faster R-CNN(Faster Region-Based CNN)models are trained with Morphological *** proposed Faster R-CNN model is trained using the augmented *** overcoming the Imbalanced Data problem,data augmentation techniques are *** Faster R-CNN performance was com-pared with existing transfer learning *** results show that the Faster R-CNN performance was signiﬁcant than other *** number of images in each class is *** example,the Neutrophil(segmented)class consists of 8,486 images,and Lymphocyte(atypical)class consists of eleven *** dataset is used to train the CNN for single-cell morphology classiﬁ*** proposed work implies the high-class performance server called Nvidia Tesla V100 GPU(Graphics processing unit).

关键词： Acute myeloid leukemia(AML) convolutional neural network(CNN) and nvidia tesla v100 gpu

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Unsupervised machine learning approach for tailoring educational content to individual student weaknesses

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High-Confidence Computing 2024年第4期4卷 91-104页

作者： Shabab Intishar Rahman Shadman Ahmed Tasnim Akter Fariha Ammar Mohammad Muhammad Nayeem Mubasshirul Haque Sriram Chellappan Jannatun Noor Department of Computer Science and Engineering BRAC UniversityDhaka 1212Bangladesh Department of Computer Science and Engineering University of South FloridaFL 33620USA Computing for Sustainability and Social Good(C2SG)Research Group Department of Computer Science and EngineeringSchool of Data and SciencesBRAC UniversityDhaka 1212Bangladesh

By analyzing data gathered through Online Learning(OL)systems,data mining can be used to unearth hidden relationships between topics and trends in student ***,in this paper,we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system *** our implementation,we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%.By focusing on marks below this threshold,we aim to identify and establish associations based on the patterns of weakness observed in the past ***,we leverage K-means clustering to provide instructors with visual representations of the generated *** strategy aids instructors in better comprehending the information and associations produced by the *** clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights,enabling them to support the verification of the relationship between *** can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their *** paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system.

关键词： Recommendation system Clustering F-P growth Apriori Associative pattern E-learning sphere Online learning

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PDF to AI Chat Bot using Python LangChain 1

PDF to AI Chat Bot using Python LangChain

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Amaresh Sekar, Janarthanan Tiwari, Govind Gupta, Pranav Galgotias University School of Computer Science and Engineering Greater Noida India School of Computer Science and Engineering Ramanagar India School of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

A novel technique called PDF-to-AI Chatbot makes it easier to transform static, textbook-based PDF texts into dynamic, interactive chatbot discussions. The necessity to transform conventional PDF papers into interesting, stoner- friendly forms is addressed by this outcome in a world where information communication is becoming less computerized and conversational. The key characteristics and advantages of the PDF-to-AI Chatbot are outlined in this abstract. Essential elements: Converting Documents This technology turns PDF texts into chatbot discourses by utilizing cutting-edge natural language processing and artificial intelligence. It preserves the integrity of the original material while identifying important information and creating conversational patterns. Interaction Stoners may easily access relevant portions and acknowledge interpretations thanks to the engaging and welcoming chatbot discussions. Individualization A more personalized and interesting reading experience is produced by the chatbot, which adjusts to stoner tastes and offers personalized recommendations based on stoner relationships. Availability on Multiple Platforms The PDF-to-AI Chatbot adds widespread availability and stoner ease by being available on vibrant digital platforms like as websites, mobile applications, and messaging services. To facilitate content optimization and data-driven decision-making, directors may aggregate perceptivity into stoner gestures and preferences, and hunters and analytics druggies can look for particular- information within papers. In summary, the PDF-to-AI Chatbot, in summary, is a significant development in the fields of document conversion and content availability. This solution solves the inescapable requirement to transform static PDF documents into dynamic and engaging chatbots as our world grows more connected and conversational. © 2024 IEEE.

关键词： Bot (Internet)

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Classification of plant diseases in images using dense-inception architecture with attention modules

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Multimedia Tools and Applications 2024年 1-26页

作者： Khan, Sultan Daud Basalamah, Saleh Naseer, Atif Department of Computer Science National University of Technology Islamabad Pakistan Department of Computer Engineering Umm Al-Qura University Makkah Saudi Arabia Department of Computer Science Umm Al-Qura University Makkah Saudi Arabia

Agriculture plays a vital role in providing food to a growing world population. However, plant diseases and pests result in 50% reductions in crop yields, which exacerbates poverty and threatens a sustainable food system. Currently, plant disease detection mainly relies on manual visual inspection and analysis of the field, which is a time-consuming and unreliable method. Modern technology, such as smart agriculture, can leverage machine learning, computer vision, and remote sensing technologies to overcome the limitations of traditional methods. Several machine learning algorithms have been used for plant disease classification and detection. However, these models suffer from limitations such as dependence on complex hand-crafted features for classification, and limited adaptability to new data. Deep learning models, on the other hand, have the ability to automatically learn hierarchical representations from raw data, making them inherently more informative and adaptable to changing data patterns. While existing deep learning approaches for plant disease detection have achieved some degree of success, these models still face challenges in achieving superior performance due to similar visual appearances of different plant diseases, intra-class differences, and variation in scale and resolution. To address these challenges, we proposed a framework that consists of hybrid network and attention module. Our hybrid module combines the benefits of DenseNet and InceptionNet to capture multi-scale features, while the attention module addresses the challenges of inter-class similarity and intra-class variability by isolating the most discriminative feature and disregarding extraneous and insignificant information. We performed experiments on publicly available challenging dataset. From Experimental results, we demonstrate the effectiveness of the proposed framework in achieving high accuracy in plant disease detection. © The Author(s), under exclusive licence to Springer Sci

关键词： Agriculture

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DGC-TnT: Enhancing Point Cloud Object Classification by Dynamic Graph Convolutions with Transformer in Transformer

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IEEE Access 2024年 12卷 111924-111931页

作者： Lin, Chien-Chou Chen, Po-Yu National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliou64002 Taiwan

Recently, deep learning neural networks have been widely used in object classification. The process of object classification typically involves extracting features from the point cloud using neural networks and integrating these features into a global feature for recognizing the object. However, most existing methods perform well in global feature extraction but still fall short in capturing local details, or lack an attention mechanism to focus on informative feature signals. This paper presents DGC-TnT which combines dynamic graph convolution (DGC) structure with the Point-TnT module. Employing a two-stage TnT architecture, DGC-TnT utilizes a self-attention mechanism to deeply analyze local features derived from dynamic graph convolution, expanding their scope globally. This mechanism effectively operates between individual points and clusters of points, thereby enhancing overall object classification performance. DGC-TnT facilitates enhanced feature communication between points and point clusters within the point cloud through the Transformer module. On the ModelNet40 and ScanobjectNN datasets, DGC-TnT has a significant improvement in accuracy. © 2013 IEEE.

关键词： Three dimensional displays

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A feature-enhanced multiscale attention approach for automated hand bone segmentation

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Multimedia Tools and Applications 2025年第16期84卷 15949-15969页

作者： Nagaraju, Y. Venkatesh Thanu Shree Yadav, P.R. Vaishnavi, A. Tejashree, S.V. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Karnataka Bengaluru560001 India

In the medical field, comprehensive analysis of bone structures is paramount for assessing skeletal health and diagnosing conditions. X-ray imaging serves as a cornerstone in bone age evaluation and the fabrication of implants. In addition to clinical applications, automated bone structure analysis aids students in studying anatomy and radiology by providing accurate and consistent representations. This automation has the potential to significantly reduce processing time and enhance students’ understanding of bone structures and their variations, thereby improving their understanding and training in the medical field. This study presents the Adaptive Multiscale Expansion Segmentation Model (AMESM), a lightweight encoder-decoder-based multiclass semantic segmentation model for segmenting hand bones. The proposed method assists physicians in tasks such as growth analysis, hand movement assessment, and implant design. The model incorporates a Multi-Scale Attention Block that improves feature representation and segmentation accuracy. The experimental results demonstrate promising performance, with a Mean Intersection over Union (mIoU) of 88.47% and a Dice Score of 93.86%. The comprehensive analysis confirms the model’s ability to achieve accurate segmentation while remaining computationally efficient. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Automation

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