Augmented MRI Images for Classification of Normal and Tumors Brain through Transfer Learning Techniques

A brain tumor is a severe malignant condition caused by uncontrolled and abnormal cell division. Recent advances in deep learning have aided the health business in Medical Imaging for the diagnosis of numerous disorders. The most frequent and widely used deep learning algorithm for visual learning and image recognition. This research seeks to multi-classification tumors in the brain from images attained by Magnetic Resonance Imaging (MRI) using deep learning models that have been pre-trained for transfer learning. As per the publicly available MRI brain tumor dataset, brain tumors identified as glioma, meningioma, and pituitary, are accounting for most brain tumors. To ensure the robustness of the suggested method, data acquisition, and preprocessing are performed in the first step followed by data augmentation. Finally, Transfer Learning algorithms including DenseNet, ResNetV2, and InceptionResNetv2 have been applied to find out the optimum algorithm based on various parameters including accuracy, precision, and recall, and are under the curve (AUC). The experimental outcomes show that the model’s validation accuracy is high for DenseNet (about 97%), while ResNetv2 and InceptionResNetv2 achieved 77% and 80% only

Forest Tree- An Efficient Proposal Approach for Data Mining

Data Mining (DM) is a way of looking on different models, summaries, & derived values from a given gathered data. DM itself work on the process of looking for analytical information in huge amount of available databases. An illustration of a predictive riddle is targeted marketing. There are many factors that influence the performance of mining on large data sets. In this paper we are going to use forest tree technique in order to improve the way of performance of how the data is to be fetched and when on implementation it will definitely overcome the performance of previous work which includes existing approach decision tree algorithm

Multipath Routing in Cloud Computing using Fuzzy based Multi-Objective Optimization System in Autonomous Networks

Intelligent houses and buildings, autonomous automobiles, drones, robots, and other items that are successfully incorporated into daily life are examples of autonomous systems and the Internet of Things (IoT) that have advanced as research areas. Secured data transfer in untrusted cloud applications has been one of the most significant requirements in the cloud in recent times. In order to safeguard user data from unauthorised users, encrypted data is stored on cloud servers. Existing techniques offer either security or efficiency for data transformation. They fail to retain complete security while undergoing significant changes. This research proposes novel technique in multipath routing based energy optimization of autonomous networks. The main goal of this research is to enhance the secure data transmission in cloud computing with network energy optimization. The secure data transmission is carried out using multi-authentication attribute based encryption with multipath routing protocol. Then the network energy has been optimized using multi-objective fuzzy based reinforcement learning. The experimental analysis has been carried out based on secure data transmission and energy optimization of the network. The parameters analysed in terms of scalability of 79%, QoS of 75%, encryption time of 42%, latency of 96%, energy efficiency of 98%, end-end delay of 45%

5G Technology based Edge Computing in UAV Networks for Resource Allocation with Routing using Federated Learning Access Network and Trajectory Routing Protocol

UAVs (Unmanned aerial vehicles) are being utilised more frequently in wireless communication networks of the Beyond Fifth Generation (B5G) that are equipped with a high-computation paradigm and intelligent applications. Due to the growing number of IoT (Internet of Things) devices in smart environments, these networks have the potential to produce a sizeable volume of heterogeneous data.This research propose novel technique in UAV based edge computing resource allocation and routing by machine learning technique. here the UAV-enabled MEC method regarding emerging IoT applications as well as role of machine learning (ML) has been analysed. In this research the UAV assisted edge computing resource allocation has been carried out using Monte Carlo federated learning based access network. Then the routing through UAV network has been carried out using trajectory based deterministic reinforcement collaborative routing protocol.We specifically conduct an experimental investigation of the tradeoff between the communication cost and the computation of the two possible methodologies.The key findings show that, despite the longer connection latency, the computation offloading strategy enables us to give a significantly greater throughput than the edge computing approach

Forest Tree Algorithm- An Efficient Approach of Data Mining Over Decision Tree

Mining of Data (DM) is a way to display different models, summaries and values derived from a given data collected. The DM itself works in the process of searching for analytical information on the large number of available databases. An example of a predictive enigma is targeted marketing. There are many factors that affect data mining performance in large data sets. In this article we will use the forest tree technique to improve performance in search for data and implementation, surely overcome the previous work performance that includes the approach of the existing tree decision tree algorithm

Design of Frequency Divider (FD/2 and FD 2/3) Circuits for a Phase Locked Loop

This paper reports on three design of Frequency Divider (FD/2) and Frequency Divider (FD 2/3) circuits. Tanner EDA tool developed on 130nm CMOS technology with a voltage supply of 1.3 V is used to build, model, and compare all circuits. For the FD/2 circuit, E-TSPC Pass Transistor logic uses 1.77 µW, whereas TSPC logic consumes 5.57 µW for the FD 2/3 circuit. It implies that the TSPC logic is the best solution since it meets the speed and power consumption requirements

A Survey on Various Congestion Control Techniques in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are made up of small battery-powered sensors that can sense and monitor a variety of environmental conditions. These devices are self-contained and fault tolerant. The majority of WSNs are built to perform data collection tasks. These data are gathered and then sent to the sink node. Small packets are sent towards the sink node in such cases, and as a result, the areas near the sink node become congested, becoming the bottleneck of the entire network. In this paper, a survey of existing techniques or methods for detecting and eliminating congestions is conducted. Finally, a comparison in the form of a table based on various matrices is presented

Different Attacks in the Network: A Review

Network security is protection of the files which can be stored information in network against hacking, misuse. Network security involves the authorization or access to data which is controlled by the network administrator. Users are assigned an ID and password or other authenticating information that allows them access to information and programs within their authority. Today anyone person can become a hacker which downloading tools from the internet. Nowadays security is becoming vital in case of networking because everyday a new kind of attack is generated which leads to compromise our network and have security in network is decreasing because of increase in number of attacks. In this paper we have shown the comparison between different types of attacks in a network in a tabular form

Sorting Technique- An Efficient Approach for Data Mining

As the new data or updates are arriving constantly, it becomes very difficult to handle data in an efficient manner. Moreover, if data is not refreshed it will soon become of no use. Hence data should be updated on regular mode so that it do not obsolete in coming future. In traditional work several other approaches or methods like page ranking, i2mapreduce( that is extension of Map Reduce) were used to enhance performance and increase computation speed as well as run-time processing. But as we have seen the performance is not up to that level which is required in current environment. So, to overcome these drawbacks, in this paper sorting technique is proposed that can enhance mean value and overall performance