DBT Recommender: Improved Trustworthiness of Ratings  through De-Biasing Tendency of Users

Mukkamula, Venu

doi:10.22266/ijies2018.0430.10

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…Vasa previa can be detected on ultrasound using a variety of techniques, including transvaginal scanning, placental cord insertion, color Doppler applied across the cervix, and three-dimensional (3D) ultrasonography [19,20,21]. When the placenta implants itself into the myometrium lining the uterine wall, a process called accretion occurs.…”

Section: Using Ultrasonography To Diagnose Placental Problemsmentioning

confidence: 99%

Investigating the abnormalities of deep learning with customized architecture using deep learning 4J

Madhu,

Nethra Betgeri,

Pavan

et al. 2024

MATEC Web Conf.

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You In most cases, doctors and the use of image processing tools can assess the placenta—the actual site of physical birth—during pregnancy. Models built using Machine Learning produce performance metrics such as Accuracy, ROC, Precision, Recall, and F-Measure, which quantify this support. This research makes use of the tailored strategy built into the Weka tool, namely the DeepLearning4j package.Using common architectures such as LeNet, VGGnet, ResNet, and Alexnet yields results that are comparable. By iterating over the input layer's Loss functions and the output layer's errors, DeepLearning4j evaluates and optimizes the suggested architecture based on its quality. The experimental results show that out of seven distinct loss functions, the one with the abbreviation "MCXENT" (meaning "Multi-class Cross Entropy") produces the most accurate and least error-prone results. Additionally, a maximum accuracy of 95.7% is obtained, which is considered the best performance. These findings lend credence to an additional machine learning strategy that employs an interactive development tool for gynecologists and offer fresh assurance in its efficacy.

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Section: Using Ultrasonography To Diagnose Placental Problemsmentioning

confidence: 99%

Investigating the abnormalities of deep learning with customized architecture using deep learning 4J

Madhu,

Nethra Betgeri,

Pavan

et al. 2024

MATEC Web Conf.

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“…Recommendation system (RS) is one of the branches in the field of information retrieval and artificial intelligence. RS is a software that functions to predict item choices based on user characteristics [38]. The purpose of the RS is to develop a mathematical model or objective function that can predict how likely users are to like an item [39].…”

Section: Recommendation Systemmentioning

confidence: 99%

Blockchain-Based Data Sharing for Decentralized Tourism Destinations Recommendation System

Arif¹,

Nurhayati²,

Kurniawan³

et al. 2020

IJIES

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One thing that tourists need to plan their tourism activities is a recommendation system. The tourism destinations recommendation system in this study has three primary nodes, namely user, server, and sensor. Each node requires the ability to share data to produce recommendations that the user expects through their mobile devices. In this paper, we propose the data-sharing system scheme uses a blockchain-based decentralized network that each node can be connected directly to each other, to support the exchange of data between them. The block architecture used in the blockchain network has three main parts, namely block information, hashes, and data. Each type of node has a different structure and direction of data communication. Where the user node sends destination assessment data to the server node, then the server node sends data from the machine learning process to the user node. The sensor sends dynamic data about popularity, traffic, and weather to the user node as consideration for finalizing the generating recommendations process. In the process of sending data, each node in the blockchain network goes through several functions, including hashing, block validation, chaining block, and broadcast. We conduct web-based experiments and analysis of the data-sharing system to illustrate the system works. The experimental results show that the system handles data circulation with an average time of mine is 84.5 ms in sending multi-criteria assessment data from the user and 119.1 ms in sending data of machine learning result from the server.

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Progressive Collaborative Method for Protecting Users Privacy in Location-Based Services

Ramakrishna Reddy,

Sharma,

Anusha

et al. 2024

MATEC Web Conf.

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The development of new mobile communication and information service technologies has opened up exciting possibilities for location-based services. Users of location-based services (LBS) can access vital data from their service providers by utilizing their location data. Maps and navigation, information services, tourist information services, social networking, and many more popular applications are available. A user's location and other personal details must be submitted to the providers of location-based services in order for them to work. For example, details about one's whereabouts and identity. By "location privacy," we mean the idea that third parties shouldn't be able to track a user's precise whereabouts. It is important that users' sensitive information be hidden from unauthorized individuals when communicating. Most difficult in LBS location-based are concerns about communications and data. Each peer does their duty reciprocally in a collaborative method, which is a completely distributed technique. For the most secure and private location-based services (LBS), it employs cryptographic methods. The number of people using LBS is growing at a rapid pace these days. At this time, there isn't a single method available that has scalability capabilities. Building a realistic and computationally efficient solution that offers high privacy while decreasing processing overhead and improving scalability is a challenging task. The suggested method is cost-effective, supports scaling, is highly resilient against security and privacy assaults, and ensures privacy.

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DBT Recommender: Improved Trustworthiness of Ratings through De-Biasing Tendency of Users

Cited by 5 publications

References 32 publications

Investigating the abnormalities of deep learning with customized architecture using deep learning 4J

Investigating the abnormalities of deep learning with customized architecture using deep learning 4J

Blockchain-Based Data Sharing for Decentralized Tourism Destinations Recommendation System

Progressive Collaborative Method for Protecting Users Privacy in Location-Based Services

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