Modeling of Chaotic Behavior of Benchmark Datasets using Hybrid Heuristic Optimization

Optimization is required for producing the best results. Heuristic algorithm is one of the techniques which can be used for finding best results. By making use of artificial neural network and particle swarm optimization values can be predicted and chaotic signals can be modeled which forms the base of this project. The chaotic signals here use are Mackey series and Box Jenkins Gas Furnace data series. The results of this work shows the comparative study of predicted number of neurons in the second hidden layer also it gives the value of mean square error while making the prediction

Substantial Phase Exploration for Intuiting Covid using form Expedient with Variance Sensor

This article focuses on implementing wireless sensors for monitoring exact distance between two individuals and to check whether everybody have sanitized their hands for stopping the spread of Corona Virus Disease (COVID). The idea behind this method is executed by implementing an objective function which focuses on maximizing distance, energy of nodes and minimizing the cost of implementation. Also, the proposed model is integrated with a variance detector which is denoted as Controlled Incongruity Algorithm (CIA). This variance detector is will sense the value and it will report to an online monitoring system named Things speak and for visualizing the sensed values it will be simulated using MATLAB. Even loss which is produced by sensors is found to be low when CIA is implemented. To validate the efficiency of proposed method it has been compared with prevailing methods and results prove that the better performance is obtained and the proposed method is improved by 76.8% than other outcomes observed from existing literatures

Probabilistic Framework Allocation on Underwater Vehicular Systems Using Hydrophone Sensor Networks

This article emphasis the importance of constructing an underwater vehicle monitoring system to solve various issues that are related to deep sea explorations. For solving the issues, conventional methods are not implemented, whereas a new underwater vehicle is introduced which acts as a sensing device and monitors the ambient noise in the system. However, the fundamentals of creating underwater vehicles have been considered from conventional systems and the new formulations are generated. This innovative sensing device will function based on the energy produced by the solar cells which will operate for a short period of time under the water where low parametric units are installed. In addition, the energy consumed for operating a particular unit is much lesser and this results in achieving high reliability using a probabilistic path finding algorithm. Further, two different application segments have been solved using the proposed formulations including the depth of monitoring the ocean. To validate the efficiency of the proposed method, comparisons have been made with existing methods in terms of navigation output units, rate of decomposition for solar cells, reliability rate, and directivity where the proposed method proves to be more efficient for an average percentile of 64%

Probabilistic Framework Allocation on Underwater Vehicular Systems Using Hydrophone Sensor Networks

This article emphasis the importance of constructing an underwater vehicle monitoring system to solve various issues that are related to deep sea explorations. For solving the issues, conventional methods are not implemented, whereas a new underwater vehicle is introduced which acts as a sensing device and monitors the ambient noise in the system. However, the fundamentals of creating underwater vehicles have been considered from conventional systems and the new formulations are generated. This innovative sensing device will function based on the energy produced by the solar cells which will operate for a short period of time under the water where low parametric units are installed. In addition, the energy consumed for operating a particular unit is much lesser and this results in achieving high reliability using a probabilistic path finding algorithm. Further, two different application segments have been solved using the proposed formulations including the depth of monitoring the ocean. To validate the efficiency of the proposed method, comparisons have been made with existing methods in terms of navigation output units, rate of decomposition for solar cells, reliability rate, and directivity where the proposed method proves to be more efficient for an average percentile of 64%

Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm

The majority of people in the modern biosphere struggle with depression as a result of the coronavirus pandemic’s impact, which has adversely impacted mental health without warning. Even though the majority of individuals are still protected, it is crucial to check for post-corona virus symptoms if someone is feeling a little lethargic. In order to identify the post-coronavirus symptoms and attacks that are present in the human body, the recommended approach is included. When a harmful virus spreads inside a human body, the post-diagnosis symptoms are considerably more dangerous, and if they are not recognised at an early stage, the risks will be increased. Additionally, if the post-symptoms are severe and go untreated, it might harm one’s mental health. In order to prevent someone from succumbing to depression, the technology of audio prediction is employed to recognise all the symptoms and potentially dangerous signs. Different choral characters are used to combine machine-learning algorithms to determine each person’s mental state. Design considerations are made for a separate device that detects audio attribute outputs in order to evaluate the effectiveness of the suggested technique; compared to the previous method, the performance metric is substantially better by roughly 67%

Prevention of Cyber Security with the Internet of Things Using Particle Swarm Optimization

High security for physical items such as intelligent machinery and residential appliances is provided via the Internet of Things (IoT). The physical objects are given a distinct online address known as the Internet Protocol to communicate with the network’s external foreign entities through the Internet (IP). IoT devices are in danger of security issues due to the surge in hacker attacks during Internet data exchange. If such strong attacks are to create a reliable security system, attack detection is essential. Attacks and abnormalities such as user-to-root (U2R), denial-of-service, and data-type probing could have an impact on an IoT system. This article examines various performance-based AI models to predict attacks and problems with IoT devices with accuracy. Particle Swarm Optimization (PSO), genetic algorithms, and ant colony optimization were used to demonstrate the effectiveness of the suggested technique concerning four different parameters. The results of the proposed method employing PSO outperformed those of the existing systems by roughly 73 percent

A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

Due to air pollution, pollutants that harm humans and other species, as well as the environment and natural resources, can be detected in the atmosphere. In real-world applications, the following impurities that are caused due to smog, nicotine, bacteria, yeast, biogas, and carbon dioxide occur uninterruptedly and give rise to unavoidable pollutants. Weather, transportation, and the combustion of fossil fuels are all factors that contribute to air pollution. Uncontrolled fire in parts of grasslands and unmanaged construction projects are two factors that contribute to air pollution. The challenge of assessing contaminated air is critical. Machine learning algorithms are used to forecast the surroundings if any pollution level exceeds the corresponding limit. As a result, in the proposed method air pollution levels are predicted using a machine learning technique where a computer-aided procedure is employed in the process of developing technological aspects to estimate harmful element levels with 99.99% accuracy. Some of the models used to enhance forecasts are Mean Square Error (MSE), Coefficient of Determination Error (CDE), and R Square Error (RSE)

Reverse phase, ion exchange, HILIC and mix-mode chromatography for the determination of metformin and evogliptin in human plasma and pharmaceutical formulations

Separation and quantification of highly polar metformin (MTF) alone or in combination with dipeptidyl peptidase-4 inhibitors, such as evogliptin (EVG), sitagliptin, saxagliptin, vildagliptin, linagliptin and teneligliptin, specifically from human plasma and pharmaceutical products is still difficult in mostly preferred ODS based RP-HPLC techniques. Since, owing to weak retention of MTF in ODS, it elutes together with the biological fluid components and drug excipients. Therefore, in this study, alternative to ODS based RP-HPLC, comparatively less known analytical techniques like strong cation exchange chromatography (SCX-3), hydrophilic interaction liquid chromatography (HILIC) and mix-mode chromatography (MMC) were comprehensively evaluated for their potentials and limitations in simultaneous quantification of newly approved EVG and MTF combination (Valera-M). In results, prolonged application of SCX-3, exhibits irreproducible and irreversible retention of MTF and EVG. While in HILIC with Cyano column, excessive acetonitrile as eluent developed the precipitation of MTF and column back pressure. Comparatively, the Acclaimed® mix-mode HILIC-1, demonstrated much promising and reproducible results for MTF and EVG separation and importantly, it can be used either reverse phase or HILIC mode. Considering the overall benefits of Acclaimed® mix-mode HILIC-1, it was used in estimation of MTF and EVG from human plasma and pharmaceutical formulations