An Content-Based Medical Image Mining System Based On Fuzzy C-Means Associate Oppositional Crow Search Optimization

In recent, Content-Based Image Retrieval (CBIR) requires remained unique on the best research areas in the ground of processor presentations. The advent of the World Wide Web, proliferation of digital cameras and as well as the use of multimedia systems for public and private use, images have become more and more common around the world. The significant objective of this research is to enhance the retrieving performance of the CBIR system by incorporating optimization techniques to predict appropriate centroid in Fuzzy C-means (FCM). The intention to incorporate an optimization technique to predict FCM centroids certainly reduces complexity and computation time. The swarm intelligence method is determined to solve the prediction of optimal FCM centers of gravity and to understand the basic methodology in implementing crow search Optimization (CSO) and particle swarm optimization (PSO) urges the development of an oppositional Crow Search Optimization (OCSO). The results show that the incorporation of OCSO into FCM shows superior results competitive techniques

An Integrative Decision Support Model for Smart Agriculture Based on Internet of Things and Machine Learning

The Internet of Things (IoT) has achieved an upset in a considerable lot of the circles of our current lives, like automobile, medical services offices, home automation, retail, ed-ucation, manufacturing, and many more. The Agriculture and Farming ventures signifi-cantly affect the acquaintance of the IoT with the world. Machine learning (ML) is a part of artificial intelligence (AI) that permits software applications to turn out to be more precise at foreseeing results without being expressly customized to do as such. It uses historical data as input to predict new result values. In the event, a specific industry has sufficient recorded information to help the machine "learn", AI or ML can create out-standing outcomes. Farming is likewise one such important industry profiting and ad-vancing from machine learning at large. ML can possibly add to the total lifecycle of farming, at all phases. This incorporates computer vision, automated irrigation, and harvesting, predicting the soil, weather, temperature, moisture values, and robots for picking off the crude harvest. In this paper, I'll work on a smart agricultural information monitoring framework that gathers the necessary information from the IoT sensors set in the field, measures it, and drives it, from where it streams to store in the cloud space. The information is then shipped off the prediction module where the necessary analysis is done using ML algorithms and afterward sent to the UI for its corresponding applica-tion

An Content-Based Medical Image Mining System Based On Fuzzy C-Means Associate Oppositional Crow Search Optimization

In recent, Content-Based Image Retrieval (CBIR) requires remained unique on the best research areas in the ground of processor presentations. The advent of the World Wide Web, proliferation of digital cameras and as well as the use of multimedia systems for public and private use, images have become more and more common around the world. The significant objective of this research is to enhance the retrieving performance of the CBIR system by incorporating optimization techniques to predict appropriate centroid in Fuzzy C-means (FCM). The intention to incorporate an optimization technique to predict FCM centroids certainly reduces complexity and computation time. The swarm intelligence method is determined to solve the prediction of optimal FCM centers of gravity and to understand the basic methodology in implementing crow search Optimization (CSO) and particle swarm optimization (PSO) urges the development of an oppositional Crow Search Optimization (OCSO). The results show that the incorporation of OCSO into FCM shows superior results competitive techniques

Privacy and Security in IOT Cloud-Based Healthcare System

One of humanity's greatest difficulties is health. In the recent decade, healthcare has gotten a lot of attention. Technology is important in healthcare not just for sensory equipment, but also for communication, recording, and display devices. It is crucial to keep track of a variety of medical markers as well as the post-operative days. As a result, the Internet of things has been used in healthcare. The patient monitoring system has recently become one of the most significant breakthroughs due to its superior technology. A current strategy is essential at this time. The underlying problem with the previous method is that in severe cases, health care professionals must be present at the patient’s location to monitor symptoms on a regular basis. To address this problem, health care professionals must develop a patient monitoring system that allows them to monitor their patients remotely. The idea is a mobile based wireless health monitoring system that might provide real-time online information on a patient's physical status. The Raspberry plays a vital role in this project, as are sensors like as temperature, pulse/heart rate, and PIR. These sensors are connected to an Arduino board which reads the sensor data and send it through a serial connection to the Raspberry Pi. The sensor data is now saved in a file on the Pi, which is then uploaded to the cloud over the Internet. Finally, the user app retrieves the data.. The same data is then transferred to the patient and doctor via Firebase to further improve treatment by obtaining patient information in a timely manner

Secure Key Pre-distribution in Wireless Sensor Networks Using Combinatorial Design and Traversal Design Based Key Distribution

Security is an indispensable concern in Wireless Sensor Network (WSN) due to the presence of potential adversaries. For secure communication in infrastructureless sensor nodes various key predistribution have been proposed. In this paper we have evaluated various existing deterministic, probabilistic and hybrid type of key pre-distribution and dynamic key generation algorithms for distributing pair-wise, group-wise and network-wise keys and we have propose a key predistribution scheme using deterministic approach based on combinatorial design and traversal design which will improve the resiliency and achieve sufficient level of security in the network.This design can be used where large number of nodes are to be deployed in the WSN

Predictive modelling of building energy consumption based on a hybrid nature-inspired optimization algorithm

Overall energy consumption has expanded over the previous decades because of rapid population, urbanization and industrial growth rates. The high demand for energy leads to higher cost per unit of energy, which, can impact on the running costs of commercial and residential dwellings. Hence, there is a need for more effective predictive techniques that can be used to measure and optimize energy usage of large arrays of connected Internet of Things (IoT) devices and control points that constitute modern built environments. In this paper, we propose a lightweight IoT framework for predicting energy usage at a localized level for optimal configuration of building-wide energy dissemination policies. Autoregressive Integrated Moving Average (ARIMA) as a statistical liner model could be used for this purpose; however, it is unable to model the dynamic nonlinear relationships in nonstationary fluctuating power consumption data. Therefore, we have developed an improved hybrid model based on the ARIMA, Support Vector Regression (SVRs) and Particle Swarm Optimization (PSO) to predict precision energy usage from supplied data. The proposed model is evaluated using power consumption data acquired from environmental actuator devices controlling a large functional space in a building. Results show that the proposed hybrid model out-performs other alternative techniques in forecasting power consumption. The approach is appropriate in building energy policy implementations due to its precise estimations of energy consumption and lightweight monitoring infrastructure which can lead to reducing the cost on energy consumption. Moreover, it provides an accurate tool to optimize the energy consumption strategies in wider built environments such as smart cities

Development and validation of stability indicating liquid chromatographic (RP-HPLC) method for estimation of ubidecarenone in bulk drug and formulations using quality by design (QBD) approach

A novel, accurate, precise and economical stability indicating Reverse Phase-High Performance Liquid Chromatography (RP-HPLC) method, was developed and validated for the quantitative determination of ubidecarenone (UDC) in bulk drug, UDC marketed formulation and UDC loaded cubosomes (CBMs) nanocarriers through Response surface methodology (RSM) design with three factors and three levels was performed to optimize the chromatographic variables followed by forced degradation studies of UDC were performed to detect degradation peak. RP-HPLC separation was achieved using mobile phase consisting of Acetonitrile:Tetrahydrofuran:Deionised water in the ratio 55:42:3 and a flow rate of 1.0 mL/min was optimized with a standard retention time (Rt) of 2.15 min, through experiment. The method was found linear in the concentration range of 5-100 µg/mL with a regression coefficient of 0.999. The limit of detection (LOD) and limit of quantification (LOQ) were found to be 3.04 µg/mL and 9.11 µg/mL, respectively

Lipid-Based Nano-Formulation Development to Enhance Oral Bioavailability of Weakly Aqueous-Soluble Drug for Obesity and Hypertension

The most practical method of drug delivery is oral administration because it has a high rate of patient compliance. However, there are significant obstacles to effective oral medication delivery, including low drug enzymatic/metabolic stability and poor water solubility. Especially in the development of drug formulations for the treatment of obesity and hypertension. This research article aims to formulate solid lipid nanoparticles (SLN) of Fucoxanthin and Ramipril by the emulsification and ultrasonication methods. The nanoparticles size, polydispersity index, and the zeta potential, among other parameters, were computed. In addition, FT-IR analysis of compatibility tests between the SLNs and the loaded drug and in vitro drug release experiments were carried out. Lipid-based nano preparations have drawn plenty of interest as efficient vehicles to increase the oral bioavailability of these kinds of medications. We observed that lipid nanoparticles, have enhanced the oral bioavailability of poorly water-soluble drugs used for obesity and hypertension. Provided the above information, formulated SLNs should be further investigated using cutting-edge scientific methodologies to improve its bioavailability

Advances in targeting cyclic nucleotide phosphodiesterases

Cyclic nucleotide phosphodiesterases (PDEs) catalyse the hydrolysis of cyclic AMP and cyclic GMP, thereby regulating the intracellular concentrations of these cyclic nucleotides, their signalling pathways and, consequently, myriad biological responses in health and disease. Currently, a small number of PDE inhibitors are used clinically for treating the pathophysiological dysregulation of cyclic nucleotide signalling in several disorders, including erectile dysfunction, pulmonary hypertension, acute refractory cardiac failure, intermittent claudication and chronic obstructive pulmonary disease. However, pharmaceutical interest in PDEs has been reignited by the increasing understanding of the roles of individual PDEs in regulating the subcellular compartmentalization of specific cyclic nucleotide signalling pathways, by the structure-based design of novel specific inhibitors and by the development of more sophisticated strategies to target individual PDE variants