Recently, the RGB-D based Human Action Recognition (HAR) has gained significant research attention due to the provision of complimentary information by different data modalities. However, the current models have experienced still unsatisfactory results due to several problems including noises and view point variations between different actions. To sort out these problems, this paper proposes two new action descriptors namely Modified Depth Motion Map (MDMM) and Spherical Redundant Joint Descriptor (SRJD). MDMM eliminates the noises from depth maps and preserves only the action related information. Further SRJD ensures resilience against view point variations and reduces the misclassifications between different actions with similar view properties. Further, to maximize the recognition accuracy, standard deep learning algorithm called as Residual Neural Network (ResNet) is used to train the system through the features extracted from MDMM and SRJD. Simulation experiments prove that the multiple data modalities are better than single data modality. The proposed approach was tested on two public datasets namely NTURGB+D dataset and UTD-MHAD dataset. The testing results declare that the proposed approach is superior to the earlier HAR methods. On an average, the proposed system gained an accuracy of 90.0442% and 92.3850% at Cross-subject and Cross-view validations respectively.
Abstract: India is the second largest populated country in the world so most of the people use public transport. During peak hours issuing tickets to each passenger manually is time consuming and tedious task and also a problem. The probability of ticket fraud is also high in this case. Since the tickets are made of papers it leads to deforestation and these tickets are of no use once when the passenger gets out of the bus. So in order to overcome all these difficulties we have a Smart ticketing system that is proposed using RFID to issue tickets to passengers. In order to ensure the passengers journey with no quarrels and mesh we employ this solution that replaces the traditional paper ticketing by electronic cards, vended through automated machine using smart cards, which improves the convenience and security of transaction. This project actually suggests a much more public friendly, automated system of ticketing with the use of RFID based tickets. Automatic Fare Collection System is implemented in this project through RFID (Smart) card. RFID card is given to the passenger and when passenger enters to the bus, he has to scan the card in the RFID reader. All the record will update automatically in the server continuously. RFID system through automated machine enables the passenger to predetermine the transport details. The control circuit is designed using arduino controller that will be interfaced with a DC motor to identify the distance travelled by the passenger to collect the fare amount. In addition a LCD is used for displaying the data.
Among the applications empowered by the Internet of Things (IoT), regular health monitoring framework is an important one. Wearable sensor gadgets utilized in IoT health monitoring framework have been producing huge amount of data on regular basis. The speed of data generation by IoT sensor gadgets is very high. Henceforth, the volume of data generated from the IoT-based health monitoring framework is also very high. So as to overcome this problem, this paper proposes adaptable three-tier architecture to store and process such immense volume of wearable sensor data. Tier 1 focuses on gathering of data from IoT wearable sensor gadgets. Tier 2 employs Apache HBase for storing substantial volume of wearable IoT sensor data in cloud computing. Likewise, Tier-3 utilizes Apache Mahout for building up logistic regression-based prediction model for heart related issues. At long last, ROC examination is performed to identify the most significant clinical parameters to get heart diseases.
To built a solution to the ever growing requirements of blood due to accidents and various health problem the system is developed for accessing the information about various blood banks, and hospitals and their blood stock. The solution should give complete information about blood donor ,and activities of hospitals and blood banks regarding the blood donation. Donors provides with registration process to maintain their information for future donations as well as to make their information available to search. In our project we need to collect the information like name, roll numbers ,bloodgroup, address ,contact number. we are going to create one page in that page having home, search, donor registration, login, contact us, about us. we need to save the information about donors and their details. if any emergency occurs their can contact us for blood donors.we are acting as a mediators between donor and receiver. Receiver can contacts us through our contact numbers(or)websites
The present online application employs a contemporary artificial intelligence (AI)-driven solution to transform the process of diagnosing skin disorders. This research uses DenseNet201 and VGG19, two of the most advanced DNN architectures, to build a Convolutional Neural Network (CNN). The enhanced predictive models, built with a dataset of 930 photos divided into ten groups and strengthened by data augmentation, produce remarkably accurate predictions for a range of skin conditions. The website's intelligent chatbot is a standout feature; it was built to answer questions about skin diagnoses, treatment options, and more. This chatbot is designed to help users understand their diagnostic results and find their way on the health journey. In addition, it keeps track of users' prediction histories, so they may learn a lot about their skin's health over time and make educated choices about their medical treatments. In addition, by giving people a place to talk about their struggles and get advice from others, the website fosters a supportive community. The emphasis here is on real human connections, which are great for learning from one another and helping one another out. Firebase facilitates efficient data administration for monitoring forecasts and engaging with the community, while Replit and Voice flow support the CNN model, chatbot, and forum, guaranteeing optimal performance. By integrating cutting-edge AI with a user-centric approach, this web application empowers users with the tools, insights, and support necessary for proactive skin health management.
The local appearance-based texture descriptors used in facial expression recognition are only partially accurate. The main reason is the existence of noise-induced distortions and weak edges. Hence, this paper proposes a new local texture descriptor called as Directional Edge Coding based Facial Expression Recognition System (DEC-FERS). DEC-FERS considers the neighbouring pixels support for determining facial expression semantics including edges, corners, lines, and curves. DEC-FERS extracts weaker edge responses through edge detection masks and discards them after encoding only more robust edge responses. Robinson Compass Mask and Kirsch Compass Mask are used to extract the edge responses from facial images. JAFFE and OU-FER databases are used for the experimental validation and the performance is assessed through recognition accuracy.
Recording supported exertion in a colossal corridor is conflicting, alarming, and it gobbles up key level of class time. To keep up a key superior to average ways from these issues, a reasonable undertaking framework utilizing immense learning structure is utilized. Recording the help of an understudy see an enormous progress in improving the likelihood of illuminating structure. Recording experience typically subject to the picture coordinating consolidations two stages: face presentation and face obvious declaration. Face certification and seeing check are well-gotten some information about issues in PC vision zone; which are beginning late not saw starting at now by genuineness of tremendous position groupings, clear light conditions, and impediments. In this paper, cutting edge face ID model is utilized to see the countenances and novel proclamation design to see faces. The proposed face request structure is shallower than the cutting edge framework and it has accomplished commensurate face assertion execution. 98.67% is assaulted on LFW and 100% on get together sitting area information. The get-together passage information was made by us for reasonable execution of the full scale structure through this effort.
Malnutrition is directly or indirectly responsible for the deaths of children younger than 5 years in many countries. Identification of malnourished children will help to prevent the risk of death and can reduce physical and health issues by taking necessary measures or treatment. The proposed system uses a Convolutional Neural Network (CNN), a Deep Learning algorithm that takes input, analyzes the images, and differentiates one from the other. The architecture we used here is AlexNet for the training process and Transfer Learning. The system takes the image of a child as the input and classifies the image into a malnourished or normal child by comparing the image with the trained model. The objective of the system is to detect malnutrition in children that can help people and healthcare providers to reduce the effects caused by malnutrition by automation implementation instead of a manual process.
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