Satellite Data Classification Based On Support Vector Machine, Rough Sets Theory & Rough-SVM

As Classification is becoming one of the most crucial tasks for various applications. Text categorization, tone recognition, image classification, micro-array gene expression are the few examples of such kind of applications. The supervised classification techniques are mostly based on traditional statistics capable of providing good results when sample size seems to tend to infinity. But in practice, only finite samples can be acquired. In this paper, an innovative learning technique, Rough Support Vector Machine (SVM), is employed on Satellite Data multi class. SVM Initiated in the early 90?s, a powerful machine technique amplified from arithmetical learning led to an outburst of interest in machine learning and have made noteworthy achievement in some field as SVM technique does not agonize the boundaries of data dimensionality and limited samples [1] & [2]. In our investigation, as the support vectors, classification are gathered by learning from the training samples are very perilous. In this paper, using various kernel functions for satellite data samples relative outcomes explained

Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network

In this paper we propose a novel procedure to construct a confidence interval for multivariate time series predictions using long short term memory network. The construction uses a few novel block bootstrap techniques. We also propose an innovative block length selection procedure for each of these schemes. Two novel benchmarks help us to compare the construction of this confidence intervals by different bootstrap techniques. We illustrate the whole construction through S\&P $500$ and Dow Jones Index datasets

Proximal femoral nail- outcome and complications: a prospective study of 125 cases of proximal femoral fractures

Background: Fractures of the proximal femur are relatively common injuries in adults and common source of morbidity and mortality among the elderly. Fractures of the proximal femur include trochantric and subtrochantric fractures. The present study was designed to evaluate and analyze the role of proximal femoral nail (PFN) in the treatment of proximal femoral fractures.Methods: It was a prospective study on 125 cases of proximal femoral fractures. The fractures were classified according to AO classification. Salvati and Wilson Score were used for functional assessment.Results: In this study at 6 months follow up, union was achieved in 123 cases, open reduction was performed in 11% of cases (14 cases). Technical and mechanical complications were noted in 21% cases (27 cases). Reoperation rate was 4% (Five cases). According to Salvati and Wilson scoring system excellent results were seen in 36% of cases (45 cases), good results in 46% cases (58 cases), fair result in 13% cases (16 cases) and poor results in 5% cases (6 cases). Conclusions: It is concluded from our study that proximal femoral nailing is an attractive and suitable implant for Proximal Femoral Fractures and its use in unstable intertrochantric fractures is very encouraging

A Heuristic-Based Appliance Scheduling Scheme for Smart Homes

The ever-growing demand for electricity in the residential sector results in creating a severe burden on electric grids. However, with the emergence of smart homes (SHs) and smart grids (SGs), this burden can be reduced to some extent. To address this issue, we propose an energy management system in this paper which manages the power requirements of SHs automatically according to the utility constraints and user priorities. The proposed system is based on a heuristic technique which considers the users priority and power available from the grid as well as distributed energy resources (DERs) for scheduling of appliances. It works by dividing the appliance scheduling problem in an SH into sub-problems for different time-slots. Results show that the proposed scheme efficiently manages the load demand of the SH with respect to power available from the utility, battery energy storage system, and user preferences

DEEP LEARNING-BASED INTRUSION DETECTION AND PREVENTION IN WIRELESS COMMUNICATION

Wireless sensor networks (WSNs) are made up of a large number of sensor nodes which collect data and send it to a centralized location. Nevertheless, the WSN has several security difficulties because of resource-constrained nodes, deployment methodologies, and communication channels. So, it is very necessary to identify illegal access in order to strengthen the safety measures of WSN. The use of network intrusion detection systems (IDS) to safeguard the network is now standard procedure for any communication system. While deep learning (DL) methods are often utilized in IDS, their efficacy falls short when faced with imbalanced attacks. An IDS based on a novel transfer deep multicolumn convolution neural network (TDMCNN) technique was presented in this study to address this problem and boost performance. The most significant features of the dataset are chosen using a cross-correlation procedure and then included into the suggested methods for detecting intrusions. The accuracy, precision, sensitivity, and specificity are used to conduct the analysis and comparison. The experimental findings verified the effectiveness of the suggested method over the status quo of deep learning models for attack detection

Optimizing horizontal scalability in cloud computing using simulated annealing for Internet of Things

The Internet of Things (IoTs) is a technology that connects sensor devices to the Internet to enable smarter and more intelligent communication. Today, many industries are using various IoT devices to create smart and intelligent environments. However, the sudden increase in demand has created a major challenge for IoT connections, known as scalability. Scalability refers to increasing and expanding the number of internet-connected devices for a specific application. To address this issue, we propose simulated annealing-based horizontal scaling to achieve faster and more efficient scaling to accommodate IoT devices. We explore different horizontal scaling methods and propose a Markov chain process to model the scaling. We then use simulated annealing to optimize the scaling visualized by the Markov chain process. Our goal is to focus on the flexible nature of horizontal scalability for adding various IoT devices and resources as needed. We have compared our proposed horizontal scalability optimization with vertical scalability, which has a built-in feature of elasticity. We have evaluated several parameters, such as cost, service rate, and transfer rate, and found that our proposal outperforms existing methods