In this letter we propose a high-throughput VLSI architecture design for H.264 high-profile context-based adaptive binary arithmatic coding (HP CABAC) decoding for HDTV applications. To speed up the inherent sequentia...
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With the prevalence of artificial intelligence, people collect data through numerous sensors and use machine learning to create models for intelligent services. However, data privacy and massive data issues are raised...
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RFID technology involves the use of RFID tags for the identification and tracking of objects using radio waves. In recent years, since RFID is applied extensively in enterprises, urging faster and larger scale on the ...
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In a non-static information exchange network,routing is an overly com-plex task to perform,which has to satisfy all the needs of the *** Defined Network(SDN)is the latest and widely used technology in the future commun...
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In a non-static information exchange network,routing is an overly com-plex task to perform,which has to satisfy all the needs of the *** Defined Network(SDN)is the latest and widely used technology in the future communication networks,which would provide smart routing that is visible *** various features of routing are supported by the information centric network,which minimizes the congestion in the dataflow in a network and pro-vides the content awareness through its mined *** to the advantages of the information centric network,the concepts of the information-centric net-work has been used in the paper to enable an optimal routing in the software-defined *** there are many advantages in the information-centric network,there are some disadvantages due to the non-static communication prop-erties,which affects the routing in *** this regard,artificial intelligence meth-odology has been used in the proposed approach to solve these difficulties.A detailed analysis has been conducted to map the content awareness with deep learning and deep reinforcement learning with *** novel aligned internet investigation technique has been proposed to process the deep reinforcement *** performance evaluation of the proposed systems has been con-ducted among various existing approaches and results in optimal load balancing,usage of the bandwidth,and maximization in the throughput of the network.
In this study, a novel approach to time series forecast is introduced in order to overcome the crucial problems of the overshoot phenomenon and the effect of volatility clustering at the same time. The prediction usin...
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computer vision plays a vital role in automating environmental analysis by enabling real-time object detection and classification in diverse conditions. Litter pollution poses significant health and environmental risk...
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computer vision plays a vital role in automating environmental analysis by enabling real-time object detection and classification in diverse conditions. Litter pollution poses significant health and environmental risks due to inefficient disposal, and manual oversight is labor intensive. Effective litter detection is crucial for large-scale environmental monitoring. However, existing models face challenges such as the complexity of detecting shadowy objects in varying lighting conditions (e.g., during rain or under sun rays), difficulty in recognizing small objects, low accuracy, and poor real-time performance. Existing two-stage detectors, such as Faster R-CNN, also struggle with these issues. This paper introduces an automated deep learning-based image processing approach for accurate litter detection across different locations, using an enhanced version of YOLOv9s called LD-YOLOv9s. Key improvements in this novel approach include replacing convolutional layers with DynConvLayer in the backbone, integrating an SDConv-ADown module to substitute down-sampling layers in the neck, and using MPD-IoU instead of CIoU. These modifications reduce the chances of overlooking small objects, such as caps or lids, which had the least class meaning in the dataset, achieving a mAP of 78.3% with an inference time of 6.7ms. A significant contribution of this work is the LD-2024 dataset, curated from indoor and outdoor environments with manually annotated images. Performance comparisons were made with several YOLO versions (YOLOv3, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9 variants) and traditional object detectors (Faster R-CNN, Center Net, Retina Net, Cascade R-CNN). Ablation studies validate the effectiveness of LD-YOLOv9s, which outperforms conventional methods, achieving a 6.3% improvement in mean average precision (mAP) over YOLOv9s on the LD-2024 dataset.
This paper proposes a new score function (SF) of interval-valued intuitionistic fuzzy values (IVIFVs) in order to overcome the shortcomings of the existing SFs of IVIFVs, which are not be able to distinguish the ranki...
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A high level of glucose in the blood over a long period creates diabetes disease. Undiagnosed diabetes may trigger other complications such as cardiovascular disease, nerve damage, renal failure, and so on. There are ...
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Mobile multicast is a research hotspot and can provide many applications. Some mobile multicast schemes have been proposed, but most of them introduce new entities and study construction algorithms of the dynamic mult...
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Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions,for the improvement of quality of education and to meet the dynamic needs of *** selection of featur...
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Student performance prediction helps the educational stakeholders to take proactive decisions and make interventions,for the improvement of quality of education and to meet the dynamic needs of *** selection of features for student’s performance prediction not only plays significant role in increasing prediction accuracy,but also helps in building the strategic plans for the improvement of students’academic *** are different feature selection algorithms for predicting the performance of students,however the studies reported in the literature claim that there are different pros and cons of existing feature selection algorithms in selection of optimal *** this paper,a hybrid feature selection framework(using feature-fusion)is designed to identify the significant features and associated features with target class,to predict the performance of *** main goal of the proposed hybrid feature selection is not only to improve the prediction accuracy,but also to identify optimal features for building productive strategies for the improvement in students’academic *** key difference between proposed hybrid feature selection framework and existing hybrid feature selection framework,is two level feature fusion technique,with the utilization of cosine-based ***,according to the results reported in existing literature,cosine similarity is considered as the best similarity measure among existing similarity *** proposed hybrid feature selection is validated on four benchmark datasets with variations in number of features and number of *** validated results confirm that the proposed hybrid feature selection framework performs better than the existing hybrid feature selection framework,existing feature selection algorithms in terms of accuracy,f-measure,recall,and *** reported in presented paper show that the proposed approach gives more than 90%accuracy on benchmark dataset that is better tha
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