Face detection and recognition (FRD) technology is a very useful tool that involves taking pictures of people's faces and assessing their biological characteristics to compare and match facial data recorded in databases. Owing to its numerous advantages, including noncontact functionality, time and attendance tracking, medical applications and enhanced security and surveillance, this technology is finding increased application in a variety of contexts. Considering that the face images captured by these devices are influenced by many factors, such as light, posture, and backdrop environment, the recognition rate of current face recognition models remains inadequate. This paper presents a model that combines the You Only Live Once (YOLO) v3 algorithm for face detection with VGG16 networks for efficient face recognition. The model is specifically made to handle scenarios in which people share facial traits and to recognize people in various settings with accuracy. This paper uses two different public datasets to train and test the proposed model, WIDER FACE dataset for YOLO v3 and the Labelled Faces in the Wild (LFW) dataset for the VGG 16 networks, the improved network model performed better in identification and is more robust. Furthermore, the YOLO v3 network scored a little lesser accuracy of 95.9% in face detection, while the VGG 16 network achieved an amazing 96.2% accuracy in face recognition.

In this research Python machine learning module sklearn has been utilized to solve the Markov model. Markov modelling of the COVID-19 dynamics with air quality index (AQI), $\mathrm{PM}_{-2.5}$, $\mathrm{NO}_2$, $\mathrm{PM}_{-10}$, and $\mathrm{O}_3$, respectively. Data of the Chhattisgarh state of India has been analyzed in two phases. In phase-1 the time duration is from March 15, 2020, to May 01, 2020, and for phase-2 it is from Jun 01, 2020, to Jul 15, 2020. It has been noticed that initially change in AQI from 103 to 84.83 changed disease dynamics, and the first cases of COVID-19 reported. In the next two fortnights March 15,2020 , and April 01,2020 , the dynamics are the same, later the AQI change from 84.83 to 63.83 , but no change reported disease dynamics from April 15, 2020, to Jul 15, 2020. In phase 1, a cyclic trend has been observed for changes concerning $\mathrm{PM}_{-2.5}$. The trends for $\mathrm{PM}_{-2.5}$, $\mathrm{NO}_2$, $\mathrm{PM}_{-10}$, and $\mathrm{O}_3$, respectively are same, but for $\mathrm{O}_3$ it is different. COVID-19 reports a negative correlation with AQI, $\mathrm{PM}_{-2.5}$, $\mathrm{NO}_2$, $\mathrm{PM}_{-10}$. Moreover, a positive correlation with $\mathrm{O}_3$. This proves that the lockdown and ban on transport activities improved AQI, $\mathrm{PM}_{-2.5}$, $\mathrm{NO}_2$, $\mathrm{PM}_{-10}$, but not $\mathrm{O}_3$.

This paper explores the field of FPGA implementation and emulation of memristor devices, providing insights into the advancements, challenges, and future directions. The paper discusses various techniques used for FPGA-based memristor emulation, emphasizing the importance of accurate memristor modeling and performance evaluation. It identifies challenges in the field, including improving accuracy, scalability, real-time adaptation, standardization, integration with design tools, and exploring novel applications. Additionally, the results of the study show that FPGAs are one of the viable solutions for emulating memristors. The study concludes that FPGA based memristor emulation holds a promise for studying memristor-based circuits and systems, with potential applications in neuromorphic computing, machine learning accelerators, and analog/mixed-signal circuit design.

All of the applications that are used in industrial processes require solutions that have a particular chemical strength of the fluids or chemicals that are being under consideration for analysis. When a full-strength solution is combined with water in the proportions that are needed, it is possible to produce the particular concentrations that are wanted. The regulation of the concentration of hydrogen peroxide which produced in an electrolysis process has been investigated over the course of this article. An examination of the impact that various controllers, such as P, PI, PID, and fuzzy logic controllers, have on the process model is presented in this work with the help of MATLAB/SIMULINK as a simulation program. Using fuzzy logic controllers showed that the rising time dropped to 0.3 seconds and the settling time to 0.4 seconds, with no overshoot or undershoot.

With the expansion of IoT platforms for smart livestock farming applications and their application to agriculture, farmers are becoming more interested. This research paper presents innovative IoT solutions for the economic propagation of animals using Raspberry Pi boards. This research has two main objectives (1) design and build a cooling system that continuously controls the temperature between 8-20 degrees Celsius and controls the oxygen content in the water between 4-8 mg/L. This control uses IOT technology to control the sending and receiving of data with the Raspberry Pi board. (2) The experimental cultivation of Chinese mitten crab in a traditional cold pond was compared to that of a new type of culture pond. The design and creation of the Chinese mitten crab culture pond and the use of IOT technology to control data transmission with Raspberry Pi boards together with temperature and oxygen sensor devices. Culture ponds have been found to cause eddy water and cause sewage and suspensions to be collected in the center of the pond and removed for their intended purpose. The design of the cooling system showed that the temperature and oxygen in the culture pond can be controlled according to its purpose. Similarly, the use of IOT technology to control the operation of temperature and oxygen sensor devices can be controlled with Raspberry Pi boards, which are ready to send and receive data via a Web server in RaspPi, and alarms can be displayed on computers and LINE applications with satisfactory results. Evaluation and experimental cultivation of Chinese mitten crab in a traditional cold pond compared to a new type of culture pond designed and created. Eighty male and 80 female Chinese mitten crabs that were one week old in a culture pond with a 1- to 6-week cycle of traditional culture had an average survival rate of 79.17%, and those in the new type of pond culture had an average survival rate of 89.58%. The evaluation also revealed that male crabs have a higher survival rate than female crabs and have a satisfactory, reliable, and objective growth rate.

In the gas and industry, erosion that is brought on by particles in pipe bends is a severe issue that can lead to failure or equipment malfunction. The computational fluid dynamics (CFD) approach is primarily utilized in the presented study in order to investigate the erosion distributions as well as particle trajectories in pipe bends under various influencing conditions. Throughout upstream petroleum production activities, crude oil as well as eroded sand from formation zones is frequently transported together via pipes up to flow stations and between flow stations and pipe. The rotator fin is propelled by flow momentum in the stream-lines which are particle-laden flow pipe walls, particularly at the elbows, causing erosive damages, which could result in costly and disastrous system failure. Thus, calculating the erosion rate while the system is operating is essential to predict failures and preventing them. Of all fittings used in the piping systems, the elbows are the most prone to experience erosions brought on by oil-carried rotator fins that veer off course and strike the walls as they pass through the bent portions of elbows. The numerical simulation-based erosion prediction model was used in order to calculate relative erosion severity so as to lessen erosive damage caused through the solid rotator fin. Physical features such as particle tracking, flow turbulence, and erosion simulation were merged in this work to create the potentials needed to fully represent the present issue. The computational simulation related to crude oil flow offers comprehensive insights, but it also allows for the avoidance of significant expenses and laborious attempts associated with conventional experiments. The new analysis provides invaluable physical information that may be utilized to assess oil recovery and employ the model as an alternate particle-laden flow management tool. Additionally, it might pinpoint limiting processes and elements; develop a computer-aided tool to optimize and design future pipe systems for increasing their lifetime by enhancing their erosion resistance, which would undoubtedly save a significant amount of cost and time.

In this paper, two distinct strategies were used to enhance problem-solving abilities. The first strategy involved developing a conjugate gradient algorithm in which several new parameters were derived and proposed. The second strategy included hybridizing the dwarf mongoose optimization (DMO) algorithm in two ways, the first using the community by taking advantage of the developed conjugate gradient algorithm that was extracted from the first strategy and obtaining the hybrid algorithm (CG-DMO) that gives better results than the results of the original algorithm. The second method is to combine the sand cat swarm optimization algorithm (SCSO) and the dwarf mongoose optimization algorithm (DMO), and a hybrid algorithm (SCSO-DMO) is obtained. The dwarf mongoose optimization (DMO) algorithm uses three mongoose social groups: the alpha group, the scout group, and the babysitter group to replicate their foraging behavior. The Alpha group underwent hybridization, using the attack method of sand cats, known for their keen hearing of low-frequency sounds and their adeptness at detecting prey by digging. This hybrid approach led to the development of an equation for identifying candidate food sites within the alpha group. The proposed algorithms (CG-DMO) and (SCSO-DMO) underwent extensive testing on standard test functions, resulting in superior results compared to the original algorithm.

This paper proposes enhancing the K-means clustering method by incorporating the Crow Search Algorithm (CSA) and Calinski-Harabasz (CH) index to address the issue of determining the optimal number of clusters and attribute selection. The proposed approach, called Crow Search Algorithm K-mean clustering (CSAK_means), aims to explore the search space more effectively to find the best solutions. The efficiency of the CSAK_means algorithm is evaluated using a comparative experimental study for five datasets from the UCI repositories: Wine, Bodega, Cmc, Zoo, and Abalone. The results confirm that the proposed method outperforms the default algorithms in terms of average feature selection performance and silhouette value.