Fusion: Practice and Applications

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2692-4048ISSN (Online) 2770-0070ISSN (Print)

Diabetes prediction system using ml & dl techniques

Nandini Gupta , Shubhangi Malik , Hardik Chawla , Surinder Kaur

Diabetes nowadays is a familiar and long-term disease. If a prediction is made early, better treatment can be provided. The preprocessing data approach is extremely useful in predicting the disease at an early stage. "Many tools are used in determining significant characteristics such as selection, Prediction, and association rule mining for diabetes. The principal component analysis method was used to select significant attributes. Our judgments denote a strong association of diabetes with body mass indicator (BMI) and glucose degree. The study implemented logistic regression, decision trees, and ANN techniques to process Pima Indian diabetes datasets and predict whether people at risk have diabetes. It was analyzed that random forest had the best accuracy of 80.52 %. Out of 500 negative records & 268 positive records, our model correctly analyzed 403 records & 216 records, respectively.

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Doi: https://doi.org/10.54216/FPA.010201

Vol. 1 Issue. 2 PP. 49-65, (2020)

The Interplay Between Missing Data and Out-of-Order Measurements using Data Fusion in Wireless Sensor Networks

Piyush K. Shukla , Ozen Ozer

Multi-data transmission is the most important processing of target detection with a reduction in delay in the transmission of the data. This may occur in certain technological circumstances, and it happens significantly often in wireless sensor networks—processing such data to keep track of and make predictions about targets of interest might result in errors due to the inherent nature of the data. The Kalman filter and other algorithms with equivalent functionality are most useful for their principal application, estimating the states of dynamic systems. This difficulty of modeling and filtering such delayed states and missing data is dealt with synergistically throughout this proposed work. This is done to ensure that the best possible results are obtained. Filtering methods similar to the optimal Kalman filter are most utilized in fusing measurement data at different levels. This relatively creative technique includes filtering delayed states while also using observations that have been randomly excluded, then putting those screened delayed states and words to use in a process that involves fusing data. One of these applications is the fusion of images. To successful the task of performance evaluation for the integrated plan, the use of numerical simulations is essential. The state delay, as well as the data that is absent at random, are both included in four distinct alternative algorithms. These algorithms are then investigated, and the results are given in this paper. Referring to the gain fusion, the H-infinity a posteriori filter, the H-infinity risk sensitive filter, and the H-infinity risk sensitive filter. To accommodate a scenario that involves MATLAB and the integration of sensor data, global filtering approaches are being updated and evaluated with the use of numerical simulations that are being carried out. In addition, we provide a nonlinear observer based on the gain of the continuous time data fusion filter. Using the Lyapunov energy function, we can conclude on asymptotic convergence in the system.  These observers are presented after the previous step. Therefore, the filtering algorithms and the observers described in the current proposed work make a definite step towards improvement for controlling state delays and randomly missing data synergistically for wireless sensor networks.

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Doi: https://doi.org/10.54216/FPA.010202

Vol. 1 Issue. 2 PP. 66-78, (2020)

Multimodal Image Fusion in Biometric Authentication

Uma Maheshwari , Kalpanaka Silingam

During this study, a unique multimodal biometric system was constructed. This system incorporated a variety of unimodal biometric inputs, including fingerprints, palmprints, knuckle prints, and retina images. The multimodal system generated the fused template via feature-level fusion, which combined several different biometric characteristics. The Gabor filter extracted the features from the various biometric aspects. The fusion of the extracted information from the fingerprint, knuckle print, palmprint, and retina into a single template, which was then saved in the database for authentication, resulted in a reduction in both the spatial and temporal complexity of the process. A novel technique for safeguarding fingerprint privacy has been developed to contribute to the study. This system integrates the unique fingerprints of the thumb, index finger, and middle finger into a single new template. It was suggested that the Fixed-Size Template (FEFST) technique may be used might develop a novel strategy for the extraction of fingerprint features. From each of the fingerprints, the minute locations of the ridge end and ridge bifurcations as well as their orientations relative to the reference points were retrieved. The primary template was derived from the fingerprint that included the greatest number of ridge ends. For the purpose of generating the combined minutiae template, the templates of the other two fingerprints were incorporated into this template. The merged minutiae template that was developed was then saved in a database so that registration could take place. During the authentication process, the system received the three query fingerprints, and those fingerprints were compared to the previously saved template.

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Doi: https://doi.org/10.54216/FPA.010203

Vol. 1 Issue. 2 PP. 79-91, (2020)