At a time when technology is spreading rapidly and widely, technology has become a necessity in daily life and practical life, and this led to the emergence of many cyber-physical systems (CPS), among which the medica...
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Diabetic Retinopathy is a disease,which happens due to abnormal growth of blood vessels that causes spots on the vision and vision *** techniques are applied to identify the disease in the early stage with different m...
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Diabetic Retinopathy is a disease,which happens due to abnormal growth of blood vessels that causes spots on the vision and vision *** techniques are applied to identify the disease in the early stage with different methods and *** Learning(ML)techniques are used for analyz-ing the images andfinding out the location of the *** restriction of the ML is a dataset size,which is used for model *** problem has been overcome by using an augmentation method by generating larger datasets with multidimensional *** models are using only one augmentation tech-nique,which produces limited features of dataset and also lacks in the association of those data during DR detection,so multilevel augmentation is proposed for *** proposed method performs in two phases namely integrated aug-mentation model and dataset correlation(***).It eliminates overfit-ting problem by considering relevant *** method is used for solving the Diabetic Retinopathy problem with a thin vessel identification using the UNET *** based image segmentation achieves 98.3%accuracy when com-pared to RV-GAN and different UNET models with high detection rate.
Smart agriculture systems leverage the possibilities offered by cutting-edge technologies such as IoT, AI, and remote sensing to revolutionize conventional farming by enhancing resource utilization, production, and cr...
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ISBN:
(纸本)9798331509675
Smart agriculture systems leverage the possibilities offered by cutting-edge technologies such as IoT, AI, and remote sensing to revolutionize conventional farming by enhancing resource utilization, production, and crop damage mitigation. Real-time monitoring of soil and crop health, predictive analytics, pest control, and precision irrigation measures are all enabled by these systems. They are able to address major Indian agriculture issues, consequently boosting yield and profitability and promoting environmental sustainability. The largescale deployment of intelligent agriculture systems will change the agriculture landscape in India and will assure long-term food security for an ever-growing population. Challenges include adequate research and future studies in order to better install and achieve smart agricultural systems to protect crops. Intelligent agriculture involves all advanced research, including science and innovations, in national development through space technologies to enhance soil quality, conserve water, and facilitate agriculture information. Space ventures will undergo improved modernization through the introduction of crop sprayers, precision gene editors, epigenetics, big data analytics, IoT, wind and photovoltaic smart energy, AI-enabled robotic applications, and wide-scale desalination technologies. Implementing digital farming systems in developing economies will help their sectors as 85 percent of the global population is set to live in developing countries by 2030. Automation will prove to be necessary since food scarcity is on the rise along with resource wastage. Control strategies such as the IoT, aerial imagery, machine learning, and artificial intelligence will boost production and prevent soil degradation. These advanced technologies are also able to alleviate such issues as plant disease detection, pesticide management, and water application. The introduction of the Internet of Things in the agricultural research world has started
In the past two decades,there has been a lot of work on computer vision technology that incorporates many tasks which implement basic filtering to image *** research areas of this field include object detection and ob...
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In the past two decades,there has been a lot of work on computer vision technology that incorporates many tasks which implement basic filtering to image *** research areas of this field include object detection and object ***,wireless communication technologies are presently adopted and they have impacted the way of education that has been *** are different phases of changes in the traditional *** of three-dimensional(3D)from two-dimensional(2D)image is one of the demanding *** human can easily perceive but making 3D using software will take time ***,the blackboard has been replaced by projectors and other digital screens so such that people can understand the concept better through ***,the computer labs in schools are now more common than ***,online classes have become a ***,transferring to online education or e-learning is not without ***,we propose a method for improving the efficiency of *** proposed system consists of twoand-a-half dimensional(2.5D)features extraction using machine learning and image ***,these features are utilized to generate 3D mesh using ellipsoidal deformation *** that,3D bounding box estimation is *** results show that there is a need to move to 3D virtual reality(VR)with haptic sensors in the field of e-learning for a better understanding of real-world ***,people will have more information as compared to the traditional or simple online education *** compare our result with the ShapeNet dataset to check the accuracy of our proposed *** proposed system achieved an accuracy of 90.77%on plane class,85.72%on chair class,and car class have 72.14%.Mean accuracy of our method is 70.89%.
The rapid explosion of Android-based devices has led to a disturbing surge in the volume and sophistication of Android malware. Effective classification of these malicious applications is essential for safeguarding us...
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The paper presents a novel framework for optimizing LoRaWAN gateway placement to enhance network performance and reliability. By integrating Network Time Protocol (NTP) for global synchronization and Precision Time Pr...
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Large language models (LLMs) have significantly impacted natural language processing (NLP), healthcare, and beyond fields. LLMs have demonstrated unprecedented abilities to understand and generate human-like text, mak...
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In social networks groups play a crucial role and making decisions based on majority consensus. Which influencer nodes should we select if our goal is to broadcast a subject in a target group and increase the number o...
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This research has been carried out to improve the identification of faults in electric motors using data processing techniques. The acoustic and vibration data were divided into smaller overlapping segments to allow a...
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The recent advancements in deep convolutional neural networks have shown significant promise in the domain of road scene parsing. Nevertheless, the existing works focus primarily on freespace detection, with little at...
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The recent advancements in deep convolutional neural networks have shown significant promise in the domain of road scene parsing. Nevertheless, the existing works focus primarily on freespace detection, with little attention given to hazardous road defects that could compromise both driving safety and comfort. In this article, we introduce RoadFormer, a novel Transformer-based data-fusion network developed for road scene parsing. RoadFormer utilizes a duplex encoder architecture to extract heterogeneous features from both RGB images and surface normal information. The encoded features are subsequently fed into a novel heterogeneous feature synergy block for effective feature fusion and recalibration. The pixel decoder then learns multi-scale long-range dependencies from the fused and recalibrated heterogeneous features, which are subsequently processed by a Transformer decoder to produce the final semantic prediction. Additionally, we release SYN-UDTIRI, the first large-scale road scene parsing dataset that contains over 10,407 RGB images, dense depth images, and the corresponding pixel-level annotations for both freespace and road defects of different shapes and sizes. Extensive experimental evaluations conducted on our SYN-UDTIRI dataset, as well as on three public datasets, including KITTI road, CityScapes, and ORFD, demonstrate that RoadFormer outperforms all other state-of-the-art networks for road scene parsing. Specifically, RoadFormer ranks first on the KITTI road benchmark. Our source code, created dataset, and demo video are publicly available at ***/RoadFormer. IEEE
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