The Sign Language Recognition System has b.en designed to capture video input, process it to detect hand gestures, and translate these gestures into readab.e text. The project consists of several key components and st...
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It takes a lot of work to develop a computerized method for classifying plants for medicinal purposes. There are countless kinds of plants found in India, and each one has its own unique set oftherapeutic advantages. ...
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On the transmission line,the invasion of foreign ob.ects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the s...
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On the transmission line,the invasion of foreign ob.ects such as kites,plastic bags,and balloons and the damage to electronic components are common transmission line *** these faults is of great significance for the safe operation of power ***,a YOLOv5 target detection method based on a deep convolution neural network is *** this paper,Mobilenetv2 is used to replace Cross Stage Partial(CSP)-Darknet53 as the *** structure uses depth-wise separab.e convolution to reduce the amount of calculation and parameters;improve the detection *** the same time,to compensate for the detection accuracy,the Squeeze-and-excitation Networks(SeNet)attention model is fused into the algorithm framework and a new detection scale suitab.e for small targets is added to improve the significance of the fault target area in the *** pictures of foreign matters such as kites,plastic bags,balloons,and insulator defects of transmission lines,and sort theminto a data *** experimental results on datasets show that themean Accuracy Precision(mAP)and recall rate of the algorithm can reach 92.1%and 92.4%,*** the same time,by comparison,the detection accuracy of the proposed algorithm is higher than that of other methods.
The Sign Language Recognition System has b.en designed to capture video input, process it to detect hand gestures, and translate these gestures into readab.e text. The project consists of several key components and st...
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ISBN:
(数字)9798331523923
ISBN:
(纸本)9798331523930
The Sign Language Recognition System has b.en designed to capture video input, process it to detect hand gestures, and translate these gestures into readab.e text. The project consists of several key components and steps: Video Processing: Using OpenCV, the system captures frames from the video input. MediaPipe processes these frames to detect and track hand landmarks in real time. OpenCV capabilities allow for efficient frameextraction and basic image processing tasks such as resizing and normalization. Hand Detection and Tracking: MediaPipe pre-trained models identify and track hand movements within the video frames. The accurate detection and tracking of the hand movements are critical for the sub.equent recognition of the sign language gestures. Sign Language Recognition: The core system is the deep learning model, trained using the TensorFlow and Keras on a dataset of sign language gestures. The model learns to classify the detected hand movements into corresponding sign language characters or words. Convolutional Neural Networks (CNNs) are typically used for task due to their effectiveness in image recognition tasks. Text Display: Once the system recognizes the signs, it converts them into text and displays the output. This can be done through a console output or a graphical user interface (GUI) built with Tkinter. The GUI provides a user friendly experience, allowing users to see the translated text in real-time.
In the healthcare sector, disease prediction must beextremely precise to allow for early, effective treatment. Our overall purpose is to use a machine learning model to predict brain tumour's at an early stage. T...
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Image super-resolution (SR) remains a crucial prob.em in computer vision to obtain high resolution images from low-resolution ones. In this paper, a novel approach towards SR is presented with the help of auto-encoder...
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ISBN:
(数字)9798331544607
ISBN:
(纸本)9798331544614
Image super-resolution (SR) remains a crucial prob.em in computer vision to obtain high resolution images from low-resolution ones. In this paper, a novel approach towards SR is presented with the help of auto-encoders, a specific kind of neural network whose task is to learn compressed representations of the high resolution images. In the context of the proposed model, there is an encoder, which, alternately, compresses the input and a decoder which reconstruct the output is proposed as capturing as well as reproducing all the details of an image. experimental results show that the proposed method is one of the most efficient and accurate ones on benchmark datasets in terms of better details preservation and artefact reduction besides reducing processing time compared to the conventional methods. This makes this approach to be flexib.e and efficient to be used in medical imaging, satellite and digital photography making it an effective solution to improving image quality and resolution.
The most requested future-oriented innovation in the world is distributed computing. Distributed storage is one of the most famous managements presented in cloud computing. Information is stored on various external se...
The most requested future-oriented innovation in the world is distributed computing. Distributed storage is one of the most famous managements presented in cloud computing. Information is stored on various external servers as opposed to the fixed servers used in traditionally located information capacity in distributed storage. All the information stored on various external servers leaves the client unattended, and no one knows exactly where the information is stored. Rejected by distributed storage vendors who claim they can protect your information, but no one trusts them. Information stored via the cloud and traveling in cleartext design within an organization is a security risk. This research study proposes a strategy for allowing clients to securely store and access information from distributed stores. This technology ensures the security and protection of information stored in the cloud. Another advantage of this strategy is that it is encrypted using cryptographic methods. In this technology, the files areencrypted using both symmetric and asymmetric algorithms, which is nothing but Hybrid encryption and as a result theend user will get a pair of keys that will help in decrypting the files. If a datab.each occurs the first layer of security will get damaged but the second layer of security helps in saving the files from datab.each.
Image super-resolution (SR) remains a crucial prob.em in computer vision to obtain high resolution images from low-resolution ones. In this paper, a novel approach towards SR is presented with the help of auto-encoder...
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In the healthcare sector, disease prediction must beextremely precise to allow for early, effective treatment. Our overall purpose is to use a machine learning model to predict brain tumour's at an early stage. T...
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ISBN:
(纸本)9781665476560
In the healthcare sector, disease prediction must beextremely precise to allow for early, effective treatment. Our overall purpose is to use a machine learning model to predict brain tumour's at an early stage. Thebrain is the most crucial organ of the human body since it regulates the operation of all other organs and aids in decision-making. It is basically the central nervous system's command post and is in charge of carrying out thebody's regular voluntary and involuntary functions. The tumour is an uncontrolled proliferation of undesirab.e tissue that has formed a fibrous mesh inside of our brain. The primary goal is to use this study to detect brain tumours at an initial stages. The study would provide a forecast indicating whether a tumour is present or not. This tool can be used as a significant asset to any medical facility dealing with brain Tumor. Using machine learning and deep learning algorithms enab.es the detection of brain tumour's. We use the algorithm Support Vector Machine (a supervised machine learning approach that can be utilized for both regression and classification prob.ems issues) out of all the machine learning algorithms since it has a greater precision and is simple to train on big datasets. In this study, the histogram of oriented gradient is used to collect the feature information from medical pictures of patients tumour's stored within thedatabase. SVM is then used to separate these photos into tumour and non-tumor pictures. by using this method, it is possib.e to predict brain tumour's quickly and with a high degree of accuracy (95%), which aids in patient treatment.
Prob.ems have arisen in allocating and managing network resources effectively because of the proliferation of IoT gadgets. This study aims to provide a deep learning-based strategy for forecasting and analyzing IoT tr...
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