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6th IEEE International Conference on Signal Processing, Computing and Control, ISPCC 2021

作者： Sharma, Abha Das, Prasenjit Patel, R B School Of Engineering And Technology Chitkara University Himachal Pradesh India Chitkara University School Of Computer Application Himachal Pradesh India Chandigarh College Of Engineering And Technology Department Of Computer Science And Engineering Chandigarh India

ISBN: (纸本)9781665425520

Wireless Sensor Networks (WSNs) has become an attraction for various academic researchers and technical communities in these modern times. The aim is to work upon key issues like better data communication, maintain network load, reliability, scalability, and augment security issues. Clustering has its own significance in WSN as it plays a vital role in energy utilization, which is one of the key concerns in any kind of network. A cluster is formed by set of nodes, the formed cluster is responsible for improving the routing structure that reduces network delay and create a network with better efficiency. Nodes in a cluster come together on the basis of fitness function. The proposed review focuses on clustering aspects like objectives that are facilitated, characteristics, taxonomy for cluster formation and classification of the optimization techniques used to enhance the clustering process in WSNs which have been proposed in previous years. © 2021 IEEE.

关键词： Energy efficiency

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An Enhanced Optimization of Automated Detection of Cardiac Abnormalities Using Deep Learning

An Enhanced Optimization of Automated Detection of Cardiac A...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Ananta Ojha Dhyan Chandra Yadav Manish Kumar Goyal Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India

Deep gaining knowledge of is a place of artificial intelligence that is becoming increasingly famous inside the scientific area. This paper provides an improved optimization of computerized detection of cardiac abnormalities the use of deep learning. particularly, the authors recommend using a convolutional neural community (CNN) to stumble on abnormalities from ECG records. They use an ensemble of models to further improve accuracy and reduce false superb quotes. moreover, they apply transfer learning techniques to higher generalize the mastering from the EEG facts. The authors take a look at their optimized set of rules on two datasets of ECG recordings and file an normal accuracy of 88.9%. This demonstrates the potential for deep getting to know techniques to end up an increasing number of reliable and sturdy for detecting cardiac abnormalities. The authors also talk the feasible directions of future studies and the potentials of deep learning for clinical safety and diagnostics in phrases of fee and efficiency.

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Maximizing the Accuracy of Time Series Analysis for the Early Detection of Rhabdomyosarcomas

Maximizing the Accuracy of Time Series Analysis for the Earl...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Ritu Shree Prabhu A Anupam Kumar Gautam Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

Rhabdomyosarcomas are a rare malignant tender tissue tumor that generally gives in younger children and teenagers. Early prognosis and remedy are essential for successful outcomes. Time collection evaluation is a valuable tool for recognizing styles and trends in medical facts, mainly for rare situations, which include Rhabdomyosarcomas. It has consequently been increasingly employed to detect early signs and symptoms of the ailment. On this look, we are conscious of investigating and optimizing techniques for time collection analysis. It is an excellent way to enhance its application and accuracy in identifying early symptoms and signs of rhabdomyosarcoma. We examine present strategies and suggest improvement techniques, along with function extraction and system mastering techniques. We further inspect the effectiveness of our strategies by conducting experiments on a dataset installed from scientific facts and literature of rhabdomyosarcoma instances. Those experiments show promising effects, indicating that our proposed strategies can considerably increase the accuracy and sensitivity of time series evaluation for the early detection of rhabdomyosarcoma and cause higher prognoses for affected sufferers. The focal point of this study is to maximize the accuracy of time series analysis for the early detection of rhabdomyosarcoma. Time collection analysis includes: • The gathering of temporal information from multiple sources. • The assessment of these records. • The interpretation of correlations between the facts points. This study aims to utilize these techniques to discover diffused adjustments in affected person information so that you can perceive the onset of the disorder in advance than would be possible with traditional techniques. The examination will expand algorithms to systematically and accurately procedure the temporal statistics and discover adjustments indicative of Rhabdomyosarcomas. In addition, the look will rent gadget learning to boost the dete

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Understanding the Dynamics of Sparse Coding in Auto-Encoders for Cancer Detection

Understanding the Dynamics of Sparse Coding in Auto-Encoders...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Thiruvenkadam T Kalyan Acharjya Manish Kumar Goyal Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India

This summary outlines how sparse coding in vehicle encoders may be used to come across most cancers. It explains that by using learning sparse representations of raw records, you can benefit from a higher understanding of the underlying dynamics of cancer improvement. Furthermore, this summary outlines two of the most commonplace packages of vehicle encoders for cancer detection: function extraction and fitness tracking. This look gives insights into the capability of car encoders for efficient cancer analysis. For this reason, it should prove to be a vital element of most cancer detection and prevention. Sparse coding has been gaining interest inside the region of auto-encoder-based total methods for most cancer detection. With the increasing complexity of deep getting-to-know fashions being used for detection, the information and mastery of this research approach moderately encoded representations have turned out to be vital. Auto-encoders are used to encapsulate and encode inputs from high-dimensional facts into a lower-dimensional representation to keep the most crucial or crucial data from the facts. A selected type of vehicle encoder, a sparse automobile encoder, is designed to analyze sparsely encoded representations of records by introducing sparsity into the hidden layer of the automobile encoder via sure specialized regularizes.

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Developing Multi-Scale Patch Representations for Low Energy Data Aggregation Using Unit Block Building Model

Developing Multi-Scale Patch Representations for Low Energy ...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Bhuvana J Ritesh Kumar Vikas Kumar Kharbas Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India

Developing Multi-Scale Patch Representations using Low energy information Aggregation is a paper examining the development of multi-scale representations from massive aggregated datasets for pc vision duties. The paper presents the low-strength records aggregation (LEDA) framework, an aggregate of electricity-green processes to extract multi-scale representations from big datasets. This paper provides a unique method for developing multi-scale patch representations for image analysis and using low-energy data aggregation. The technique utilizes records aggregation to create texture capabilities from which neighbourhood patch representations are constructed. These representations are then used to assemble more than one scale of features for analysis in pc vision structures. The proposed approach is evaluated for texture segmentation, and the consequences endorse that the patch representations received from the proposed approach outperform those acquired from conventional strategies. The paper additionally discusses the consequences of using different levels of information aggregation for generating patch representations. Subsequently, the paper explores the want for further studies to assess the overall performance gains acquired from the proposed method.

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Constructing Conditional Random Fields for Automated Land Cover Mapping from Hyper Spectral Images

Constructing Conditional Random Fields for Automated Land Co...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Sunil Sharma Prabha S Vaishali Singh Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

This paper proposes an automatic land cover mapping technique using Conditional Random Fields (CRFs) from hyper-spectral snapshots. It introduces a hierarchical feature vector method to encode the labels' spatial relationships. The paper opinion associated paintings on land cover mappings and introduces the idea of CRFs inside the context of the land cowl category. A description of the facts, pre-processing techniques, and the parameters used for schooling and checking out the model is supplied. The paper develops and evaluates the CRF model, evaluating the results of other land cowl class strategies. Finally, the authors discuss the capacity packages of this technique in the context of other land cowl mapping obligations. This painting is innovative in its method to land cowl mapping and offers a successful implementation of the CRF model for this hassle. It explores using Conditional Random Fields (CRFs) to facilitate automatic land cover mapping from hyperspectral photos (HSI). The CRF method was implemented to estimate the land cover labels in an unsupervised gaining knowledge of style. To this stop, the pixel-level land cowl information for a given area changed into modeled as a graphical version, permitting place-degree marginal probability computations for a dense prediction. Experimental outcomes validated that the proposed technique ought to obtain a sizeable development in the accuracy of land cover mapping for the tested records set compared to its baseline. The proposed CRF-primarily based land cowl mapping method exhibited a higher convergence price and better nearby minima convergence. Moreover, the proposed approach was located to be sturdy in phrases of sensitivity to preliminary land cowl labels.

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The Impact of AI-Powered Personalization on Academic Performance in Students

The Impact of AI-Powered Personalization on Academic Perform...

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Recent Trends in computer science and Technology (ICRTCST), International Conference on

作者： Shweta G. Shete Priyanka Koshti Vinayak I. Pujari Department of Artificial Intelligence and Machine Learning Sanjay Ghodawat University Kolhapur Department of Computer Science Sanjay Ghodawat University Kolhapur Department of Computer Application D.Y. Patil School of Engineering and Management Kolhapur

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

This study investigates how learners’ performance is affected by AI-driven personalization. This goal compares students' academic performance in AI-driven personalized learning environments to that of students in conventional learning environments. This study shows the rapid development and application of artificial intelligence (AI) in education has led to the emergence of personalized learning platforms that utilize AI to provide tailored learning experiences for individual students. The study shows a beneficial relationship between enhanced academic accomplishment, engagement, and satisfaction with individualized AI-based adaptive learning through quantitative and qualitative analysis. The results demonstrate how AI-driven customization can improve student achievement and change the way that education is delivered.

关键词： Training computer science Adaptive learning Technological innovation Ethics Learning (artificial intelligence) Transforms

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WheatNet: Attentional Path Aggregation Feature Pyramid Network for Precise Detection and Counting of Dense and Arbitrary-Oriented Wheat Spikes

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 606-616页

作者： Jiao, Lin Liu, Qihuang Liu, Haiyun Chen, Peng Wang, Rujing Liu, Kang Dong, Shifeng Anhui University School of Internet Hefei230031 China Chinese Academy of Sciences National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Hefei230031 China Chinese Academy of Sciences Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Science and Technology of China Hefei230031 China Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Internet Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Chinese Academy of Science Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Sheffield Department of Computer Science SheffieldS10 2TN United Kingdom Institute of Dataspace Hefei230093 China

Achieving the precise and real-time detection of wheat spikes play a crucial role in wheat growth monitoring for precision agriculture community. Machine-learning methods are commonly introduced to automatically detect and count the wheat spikes, which need carefully selected hand-crafted feature descriptors, leading to time-consuming and poor performance. The deep learning has become a promising technology for the accurate detection wheat spikes, owing to its powerful ability of feature extraction. However, the obtained wheat spike images from UAV still have serious overlap, dense distribution, various orientations, and large aspect ratios, leading to poor performance of recent wheat spike detection method. To address the demand of precise and fast detection and counting of wheat spike with dense distribution and arbitrary-orientation, a novel deep learning-based method, WheatNet, has been proposed. The attention mechanism has been introduced the process of feature fusing to highlight the important features of wheat spike as well as inhibit the useless information. Additionally, to optimize the parameters of the network, a loss function with soft dynamic label assignment is adopted to reduce the number of low-quality matches, which provides significant performance gains over other wheat spike detectors. Furthermore, to achieve the precise detection of wheat spike with multi-orientations, a large-scale oriented wheat spike dataset has been constructed, named RoWheat, including 900 images and 50419 annotations with dense distribution and various orientation. Experimental studies demonstrate that the proposed WheatNet achieves a recall of 99.7% and mAP of 91.8%, showing its promising performance gain compared to other state-of-the-art methods. © 2023 IEEE.

关键词： Image annotation

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Fog Computing in IoT: Architecture and applications

Fog Computing in IoT: Architecture and Applications

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International Conference on science Technology engineering and Management (ICONSTEM)

作者： Anurag Vijay Agrawal Shashi Gowridurga A Suresh Balakrishnan T Anwar Ahamed Shaikh Kalaipriya V Department of Electronics and Communication Engineering J. P. Institute of Engineering and Technology Meerut Uttar Pradesh India Department of Computer Application CCS University Meerut Department of Computer Science and Engineering Velammal Institute Of Technology Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Tamil Nadu India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering RMK College of Engineering and Technology Puduvoyal Tamil Nadu India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

This exploration paper explores the paradigm of Fog Computing as a transformative armature in the Internet of effects(IoT) geography. Fog Computing represents a decentralized approach, positioning computing coffers closer to the edge of the network, thereby addressing quiescence, bandwidth, and data sequestration challenges current in traditional polycentric IoT infrastructures. The paper provides an in-depth analysis of Fog Computing's armature, expounding its crucial factors similar to fog bumps, middleware, and edge bias. likewise, it investigates the different operations of Fog Computing in IoT, ranging from realtime data analytics, effective resource operation, to enhanced security and sequestration measures. By bridging the gap between edge bias and centralised pall waiters, Fog Computing emerges as a vital result, fostering the elaboration of scalable, effective, and responsive IoT ecosystems. This exploration contributes precious perceptivity to the architectural nuances and practical operations, paving the way for the integration of Fog Computing as a foundation in the ever-expanding realm of IoT technologies.

关键词： Geography Data analysis Ecosystems Mathematics Distance measurement Internet Security

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WOBTree:a write-optimized B+-tree for non-volatile memory

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Frontiers of computer science 2021年第5期15卷 37-51页

作者： Haitao WANG Zhanhuai LI Xiao ZHANG Xiaonan ZHAO Song JIANG School of Computer Science and Engineering Northwestern Polytechnical UniversityXi'An 710072China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityMinistry of Industry and Information TechnologyXi'An 710072China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’An 710072China Department of Computer Science and Engineering University of Texas at ArlingtonArlingtonTX 76010USA

The emergence of non-volatile memory(NVM)has introduced new opportunities for performance optimizations in existing storage *** better utilize its byte-addressability and near-DRAM performance,NVM can be attached on the memory bus and accessed via load/store memory instructions rather than the conventional block *** this scenario,a cache line(usually 64 bytes)becomes the data transfer unit between volatile and non-volatile ***,the failure-atomicity of write on NVM is the memory bit width(usually 8 bytes).This mismatch between the data transfer unit and the atomicity unit may introduce write amplification and compromise data consistency of node-based data structures such as B+-*** this paper,we propose WOBTree,a Write-Optimized B+-Tree for NVM to address the mismatch problem without expensive *** minimizes the update granularity from a tree node to a much smaller subnode and carefully arranges the write operations in it to ensure crash consistency and reduce write *** results show that compared with previous persistent B+-tree solutions,WOBTree reduces the write amplification by up to 86× and improves write performance by up to 61× while maintaining similar search performance.

关键词： non-volatile memory B+-tree atomic granularity mismatch write amplification performance optimization

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