The mobile nodes are infrequent movement in nature; therefore, its packet transmission is also infrequent. Packet overload occurred for routing process, and data are lossed by receiver node, since hackers hide the normal routing node. Basically, the hidden node problem is created based on the malicious nodes that are planned to hide the vital relay node in the specific routing path. The packet transmission loss occurred for routing; so, it minimizes the packet delivery ratio and network lifetime. Then, proposed enhanced self-organization of data packet (EAOD) mechanism is planned to aggregate the data packet sequencially from network structure. The hacker node present in routing path is easy to separate from network with trusty nodes. In order to secure the regular characteristics of organizer node from being confirmed as misbehaving node, the hidden node detection technique is designed for abnormal routing node identification. This algorithm checks the neighboring nodes that are hacker node, which hide the trust node in the routing path. And that trust nodes are initially found based on strength value of every node and assign path immediately. It increases network lifetime and minimizes the packet loss rate.

The main purpose of the work is to evaluate the deep machine learning algorithms used for the distinction between weeds and crop plants using the open database of images of the carrot garden. Precision farming methods are highly prevalent in the agricultural environment and can embed intelligent methods in drones and ground vehicles for real-time operation. In this work, the accuracy of the weed and crop segment is analyzed using two different frameworks of deep learning for the semantic segment: the fully convolutional network and the ResNet. An open database with images of 40 plants and weeds was used for the case study. The results show a global accuracy of more than 90% in the verification package for both structures. In the second experiment, new FCN networks were trained to evaluate the impact of these processes on different image preprocessing and separation performance by different training/testing rates of the dataset.

Lung cancer is a form of carcinoma that develops as a result of aberrant cell growth or mutation in the lungs. Most of the time, this occurs due to daily exposure to hazardous chemicals. However, this is not the only cause of lung cancer; additional factors include smoking, indirect smoke exposure, family medical history, and so on. Cancer cells, unlike normal cells, proliferate inexorably and cluster together to create masses or tumors. The symptoms of this disease do not appear until cancer cells have moved to other parts of the body and are interfering with the healthy functioning of other organs. As a solution to this problem, Machine Learning (ML) algorithms are used to diagnose lung cancer. The image datasets for this study were obtained from Kaggle. The images are preprocessed using various approaches before being used to train the image model. Texture-based Feature Extraction (FE) algorithms such as Generalized Low-Rank Models (GLRM) and Gray-level co-occurrence matrix (GLCM) are then used to extract the essential characteristics from the image dataset. To develop a model, the collected features are given into ML classifiers like the Support Vector Machine (SVM) and the k-nearest neighbor's algorithm (k-NN).

A brain tumor is defined by the proliferation of aberrant brain cells, some of which may progress to malignancy. A brain tumor is usually diagnosed via a magnetic resonance imaging (MRI) examination. These images demonstrate the recently observed aberrant brain tissue proliferation. Several academics have examined the use of machine learning and Deep Learning (DL) algorithms to diagnose brain tumors accurately A radiologist may also profit from these forecasts, which allow them to make more timely decisions. The VGG-16 pre-trained model is employed to detect the brain tumor in this study. Using the outcomes of training and validation, the model is completed by employing two critical metrics: accuracy and loss. Normal people confront numerous challenges in scheduling a doctor's appointment (financial support, work pressure, lack of time). There are various possibilities for bringing doctors to patients' homes, including teleconferencing and other technologies. This research creates a website that allows people to upload a medical image and have the website predict the ailment. The Google Cloud Platform (GCP) will be utilized to install the DL model due to its flexibility and compatibility. The customized brain tumor detection website is then constructed utilizing HTML code.

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