The proposed system consists of an Artificial Intelligent software that is capable of counting the number of people from the user inputs such as images and video frames more accurately. The system uses the advanced Mo...
The proposed system consists of an Artificial Intelligent software that is capable of counting the number of people from the user inputs such as images and video frames more accurately. The system uses the advanced MobileNet Single Shot Detection algorithm to detect the human class in the given frame at the given moment. The count of individuals in the frame is dependent on the threshold set by the end-user. The primary goal of the application is to provide an easy-to-use and efficient tool for analyzing images and videos. The model used for object detection is trained on the mall dataset from Kaggle website and is capable of accurately detecting people in real-time, making it suitable for a wide range of applications. The people in the frame are tracked using centroid tracker algorithm, and the tracked classes are counted. The detection algorithm is built using the popular library keras for its versatile usage. This system uses Keras, a high-level deep learning library, and OpenCV, a computer vision library, to perform real-time people counting. The system utilizes object detection algorithms to detect and track individuals, and then increments a count each time a person crosses a designated line. The system is capable of handling multiple people simultaneously and accurately counting them in real-time.
Crowd counting is a computer vision task that focuses on accurately estimating the number of people present in a given scene. In the past few years, convolutional neural network-based deep learning techniques have ach...
Crowd counting is a computer vision task that focuses on accurately estimating the number of people present in a given scene. In the past few years, convolutional neural network-based deep learning techniques have achieved remarkable success in many computer vision tasks, including crowd counting. In the field of crowd counting, large-scale changes have always been a great challenge. To resolve this problem, previous work used multiple branches to obtain information at different scales and combined it. However, purely combining multi-branch features cannot effectively utilize multi-scale information. In this work, we modify the previous multi-branch architecture, which can reasonably select the appropriate scale information. Furthermore, we test our model on the ShanghaiTech dataset and demonstrate the competitive performance of our method.
Recently, identifying abnormalities via medical images became vital. As these images are transferred across the IoMT network, they require high safety. Unauthorised use of the data contained in these images might have...
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Cloud-based data processing latency mainly depends on the transmission delay of data to the cloud and the used data processing algorithm. To minimize the transmission delay, it is important to compress the transferred...
Cloud-based data processing latency mainly depends on the transmission delay of data to the cloud and the used data processing algorithm. To minimize the transmission delay, it is important to compress the transferred data without reducing the quality of the data. When using data compression algorithms, it is important to validate the impact of these algorithms on the detection quality. This work evaluates the effects of image compression and transmission over wireless interfaces on state of the art neural networks. Therefore, a modern image processing platform for next generation automotive processing architectures, as used in software defined vehicles, is introduced. The impacts of different image encoders as well as data transmission parameters are investigated and discussed.
In today’s society, when the internet is pervasive, everyone gets their news from a variety of online sources. Information can swiftly reach millions of users in a short amount of time due to the growing usage of soc...
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ISBN:
(数字)9798350388602
ISBN:
(纸本)9798350388619
In today’s society, when the internet is pervasive, everyone gets their news from a variety of online sources. Information can swiftly reach millions of users in a short amount of time due to the growing usage of social media sites like Facebook, Twitter, and others. Fake news has far-reaching effects, ranging from swaying election outcomes in favor of particular politicians to fostering prejudiced viewpoints. The primary platforms for disseminating fake news are Instagram, WhatsApp, and numerous other social media platforms. By presenting a machine learning-based fake news detection algorithm, this work provides a remedy. This model needs fictitious data that was gathered from multiple information sources. Data is extracted using web analytics, which is mostly used to build datasets. Real data sets and fake data sets are the two primary categories into which the data is separated. Random forest, logistic regression, decision trees, KNN, and gradient boosting are the classifiers used to classify the data. The data is categorized as true or false data based on the version that was received. The user will be able to determine whether or not the information on the website is fraudulent based on this.
Compensation mechanisms are used to counterbalance the discomfort suffered by users due to quality service issues. Such mechanisms are currently used for different purposes in the electrical power and energy sector, e...
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Osteoporosis is a prevalent bone disease that can be crippling and is distinguished by a loss of bone strength and density. Fractures and other osteoporosis problems can be prevented by an early diagnosis of the condi...
Osteoporosis is a prevalent bone disease that can be crippling and is distinguished by a loss of bone strength and density. Fractures and other osteoporosis problems can be prevented by an early diagnosis of the condition. In this context, bone image analysis using X-rays and CT scans is a promising approach for detecting osteoporosis at an early stage. This research paper presents a method for the early osteoporosis identification by bone image analysis. The proposed method combines traditional image processing techniques with Convolutional Neural Networks (CNN). A diverse dataset of bone images is collected and preprocessed. Conventional image processing techniques are used for feature extraction, while a CNN model is developed and trained for classification. The experimental results demonstrate high accuracy, sensitivity, and specificity in detecting osteoporosis. The proposed method is non-invasive, cost-effective, and scalable, which makes it a valuable tool for enhancing osteoporosis diagnosis and patient treatment.
High order modulation formats constitute the most prominent way for increasing spectral efficiency in transmission systems. Coherent transceivers that support such higher order formats require heavy digital signal pro...
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This paper presents a design scheme for a passive wireless temperature measurement system in high-temperature environments. A slotted cylindrical resonator is used as the sensor, and a high-gain horn antenna is used a...
This paper presents a design scheme for a passive wireless temperature measurement system in high-temperature environments. A slotted cylindrical resonator is used as the sensor, and a high-gain horn antenna is used as the external excitation source. Simulation results show that the system can achieve a test distance of 58 mm and sensitivity up to 386.35 MHz −1 @17.8 GHz. The proposed temperature measurement system has a simple structure, high sensitivity, and long contact distance, and it can be used for temperature measurement from 25 °C to 700 °C in harsh environments.
Many meta heuristic optimization techniques have been developed to address exceedingly difficult optimization issues in numerous disciplines. Fireworks Algorithm (FWA) was proposed as a Swarm Intelligence Optimization...
Many meta heuristic optimization techniques have been developed to address exceedingly difficult optimization issues in numerous disciplines. Fireworks Algorithm (FWA) was proposed as a Swarm Intelligence Optimization technique, mimicking fire work explosions. This paper proposes using FWA in the field of Agricultural Drought Assessment. Droughts are complex natural disasters that need efficient mitigation techniques. Latest research in existing drought assessment tools suggest utilizing Convolutional Neural Network (CNN) models, often hybridized with other optimization strategies. Considering the four vegetation indices Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI) and Soil Adjusted Vegetation Index (SAVI), a hybridized model that integrates FWA into CNN is proposed. This enhanced model can give up to 94% accuracy with very minimal loss for prediction of future drought. This model improves efficiency significantly.
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