Перегляд за Автор "Paithane, Pradip M."

Зараз показуємо 1 - 3 з 3
  • Ескіз
    ДокументНевідомий
    Automatic pancreas segmentation using ResNet-18 deep learning approach
    (КПІ ім. Ігоря Сікорського, 2022) Kakarwal, S. N.; Paithane, Pradip M.
  • Ескіз
    ДокументНевідомий
    Novel modified kernel Fuzzy C-Means algorithm used for cotton leaf spot detection
    (КПІ ім. Ігоря Сікорського, 2023) Paithane, Pradip M.; Sarita Jibhau Wagh
    . Image segmentation is a significant and difficult subject that is a prerequisite for both basic image analysis and sophisticated picture interpretation. In image analysis, picture segmentation is crucial. Several different applications, including those related to medicine, facial identification, Cotton disease diagnosis, and map object detection, benefit from image segmentation. In order to segment images, the clustering approach is used. The two types of clustering algorithms are Crisp and Fuzzy. Crisp clustering is superior to fuzzy clustering. Fuzzy clustering uses the well-known FCM approach to enhance the results of picture segmentation. KFCM technique for image segmentation can be utilized to overcome FCM’s shortcomings in noisy and nonlinear separable images. In the KFCM approach, the Gaussian kernel function transforms high-dimensional, nonlinearly separable data into linearly separable data before applying FCM to the data. KFCM is enhancing noisy picture segmentation results. KFCM increases the accuracy rate but ignores neighboring pixels. The Modified Kernel Fuzzy C-Means approach is employed to get over this problem. The NMKFCM approach enhances picture segmentation results by including neighboring pixel information into the objective function. This suggested technique is used to find “blackarm” spots on cotton leaves. A fungal leaf disease called “blackarm” leaf spot results in brown leaves with purple borders. The bacterium can harm cotton plants, causing angular leaf blotches that range in color from red to brown.
  • Ескіз
    ДокументНевідомий
    Ptimization of route distance using k-nn algorithm for on-demand food delivery
    (КПІ ім. Ігоря Сікорського, 2023) Paithane, Pradip M.; Wagh, Sarita Jibhau; Kakarwal, Sangeeta N.
    Customers are now more able to purchase goods over the phone or the Internet, and the ability for those purchases to be delivered safely to the customer’s location is proliferating. On-request meal delivery, where customers submit their food orders online, and riders deliver them, is growing in popularity. The cuttingedge urban food application necessitates incredibly efficient and adaptable continuous delivery administrations toward quick delivery with the shortest route. However, signing up enough food parcels and training them to use such food-seeking frameworks is challenging. This article describes a publicly supported web-based food delivery system. IoT (Internet of Things) and 3G, 4G, or 5G developments can attract public riders to act as publicly sponsored riders delivering meals using shared bikes or electric vehicles. The publicly funded riders are gradually distributed among several food suppliers for food delivery. This investigation promotes an online food ordering system and uses K-Nearest Neighbor calculations to address the Traveling Salesman Problem (TSP) in directing progress. The framework also uses the Global Positioning System (GPS) on Android-compatible mobile devices and the TOMTOM Routing API to obtain coordinates for planning purposes. To evaluate the presentation of the proposed approach, recreated limited scope and certifiable enormous scope on-request food delivery occurrences are used. Compared to the conventional methodology, the proposed strategy reduces the delay time. Each rider will receive the most direct route to the order delivery address. The delivery delay time is reduced by approximately 10–15 minutes for every order. The food supplier can determine whether an item is available to the rider; thus, the food supplier can add an order to the rider having the shortest way.