Transmission control protocol (TCP) is the most common protocol found in recent networks to maintain reliable communication. The most popular transport protocol in use today is TCP that cannot fully utilize the ability of the network because of the constraints of its conservative congestion control algorithm and favors reliability over timeliness. Despite congestion is the most frequent cause of lost packets, transmission defects can also result in packet loss. In response to packet loss, end-to-end congestion control mechanism in TCP limits the amount of remarkable, unacknowledged data segments that are permitted in the network. To overcome the drawback, Optimized Extreme Gradient Boosting Algorithm is proposed to predict the congestion. Initially, the data is collected and given to data preprocessing to improve the data quality. Min-Max normalization is used to normalize the data in the particular range and KNN-based missing value imputation is used to replace the missing values in the original data in the preprocessing section. Then the preprocessed data is fed into the Optimized Extreme Gradient Boosting Algorithm to predict the congestion. Remora optimization is used in the designed model for optimally selecting the learning rate to minimize the error for enhancing the prediction accuracy in machine learning. For validating the proposed model, the performance metrics attained by the proposed and existing model are compared. Accuracy, precision, recall and error values for the proposed methods are 96%, 97%, 96% and 3% values are obtained. Thus, the proposed optimized extreme gradient boosting with the remora algorithm for congestion prediction in the transport layer method is the best method than the existing algorithm.

The use of predictive analytics or models in healthcare has the potential to revolutionize patient care by identifying high-risk patients and intervening with targeted preventative measures to improve health outcomes. This makes the application of analytics in healthcare a concept of utmost interest, which has been explored in various fashions by several scholars. From predicting patients’ ailments to prescribing appropriate drugs, predictive models have seen massive interest. This work studied published works on predictive models in healthcare and observed that the implementation of predictive models in healthcare is experiencing a notable upswing, with a particular focus on research in the United States, where a majority of the top publications originated. Surprisingly, all of the leading nations in this sector have affiliations spanning many continents, with the exception of Africa and South America, together producing a substantially larger volume of research than other countries. The United States also shone out, accounting for 60% of the top five researchers. Notably, although it was published in 2017 (relatively later), Jiang et al. had the most citations (1,346). These studies' core themes were clinical standards, machine learning terminology, and model accuracy. The Journal of Biomedical Informatics topped among journals, with 54 articles, while Luo Gang emerged as the top-performing author, with 12 publications.

Assuring product safety and quality in the explosives manufacturing process is critical today to protect worker and environmental safety. Temperature and humidity in the manufacturing plant are critical factors to consider because they can impact the manufacturing process and the quality of the final product. In this work, we design a temperature and humidity monitoring system for 31 explosive materials company using ethernet communication standard. In explosives factories, this communication standard is more suitable than other commonly used wireless communication technologies. We tested the system at 31 explosive materials factory. Test results show that the system operates stably and accurately. This system assists factory operators in increasing production efficiency, reducing dangers, and ensuring the quality of explosives.

Agriculture is one of the most prominent industries which guarantee food requirements and employment throughout the globe due to huge land availability, and atmospheric conditions. But nowadays, security of the available resources are the major concerns due to damage caused by objects inside the agriculture field. There are many traditional algorithms for object detection, but they are not very effective in terms of real time environments. Hence, a deep learning-based object detection model is generated by enhancing YOLOv3. The process involved firstly, k-means clustering was used to identify clusters, followed by modifying the convolutional neural network layers. Additionally, the batch and subdivision values of the actual YOLOv3 model were optimized under the darknet53 framework. The architecture was also configured to detect eleven classes of objects, ensuring that the model could identify a broad range of objects. The experimental results demonstrate that the Delta model achieved a remarkable increase in accuracy from 75.19% to 95.86%. In addition, the model outperformed other models in terms of precision(97%), recall(96%), F1_Score(96%), IoU(80.81%), and mAP(95.86%). Based on these findings, it can be concluded that the delta model offers superior detection capabilities and lower computational complexity compared to conventional methods used in the agriculture field.

Block chain is a computerized data set containing data (like records of monetary exchanges) that can be at the same time utilized and shared inside an enormous decentralized, openly open organization. Block chain development has been a prominent occurrence of changing the statutes of wellbeing in money related trades and information exchange. It offers an extraordinary development for data integration with security. Block chain relies upon the norms of understanding, decentralization, and cryptography for following the trust in trades. In any case, block chain security issues have continued to agitate various affiliations and early adopters. It is sure that, even the grounded block chain new organizations experience burdens in block chain security. Without a doubt, block chain innovation has seen a far-reaching adaption lately. Aside from beginning adaption into digital currencies, today it is being utilized in medical care, land, shrewd contacts, and so forth.  The ill-advised execution of innovation has been the reason for some block chain block protection concern, which can put the block chain vulnerable and can permit the aggressors to play out a few noxious exercises. To address the secrecy to the sensitive information in the Block chain organization, a proposed method namely progressive secure key administration for Block-Chain Technology with Lagrange Interpolation (PKABCLI) has been presented in this paper.

This research suggests an efficient idea that is better suited for speech processing applications for retrieving the accurate pitch from speech signal in noisy conditions. For this objective, we present a fundamental frequency extraction algorithm and that is tolerant to the non-stationary changes of the amplitude and frequency of the input signal. Moreover, we use an accumulated power spectrum instead of power spectrum, which uses the shorter sub-frames of the input signal to reduce the noise characteristics of the speech signals. To increase the accuracy of the fundamental frequency extraction we have concentrated on maintaining the speech harmonics in their original state and suppressing the noise elements involved in the noisy speech signal. The two stages that make up the suggested fundamental frequency extraction approach are producing the accumulated power spectrum of the speech signal and weighting it with the average magnitude difference function. As per the experiment results, the proposed technique appears to be better in noisy situations than other existing state-of-the-art methods such as Weighted Autocorrelation Function (WAF), PEFAC, and BaNa.

The rapid improvement of sensing and recognition technology has had an impact on the vehicle sector that led to the development of self-driving cars. Thus, vehicles are capable of driving themselves without human interaction, mostly relying on cameras, various sensors technology, and advanced algorithms for navigation. In this research, a controller module of a self-driving car project is proposed using a rule baser reasoner that is capable to drive a car considering the health condition of the driver, the road lanes, traffic signs, obstacles created by other vehicles. A number of sensors including Global Positioning System module, camera, compass, ultrasonic sensor, physiological sensor (heartbeat, blood pressure, body temperature, etc.) are involved while reasoning. The proposed controller consists of several modules: sensor module, lane detection module, road sign and human detection module, reasoning module, Instruction execution module. According to the experimental results the proposed system is able to make correct decisions with a success rate of about 90-95%.

Fractional calculus plays a crucial role in the representation of various natural and physical phenomena by incorporating the inherent non-locality and long-term memory effect of fractional operators. These models offer a more precise and systematic depiction of the underlying phenomena. The focus of this research paper is on the utilization of fractional calculus in the context of the epidemic model. Specifically, the model considers a fractional order ρ, where 0<ρ≤1, and employs the Caputo fractional order derivative to describe the transmission of malware in both wireless and wired networks. The basic reproduction number, along with the fractional order ρ, is identified as the threshold parameter in this model. The stability of the system is analysed at different stages of the reproduction number, considering both local and global asymptotic stability. Additionally, sensitivity analysis is conducted on the model parameters to determine the direction of change in the reproduction number. This analysis aids in understanding whether the reproduction number will increase or decrease under different scenarios. To obtain numerical results, the Fractional Forward Euler Method is utilized for simulation purposes. This method enables the computation of the model's dynamics and offers insights into the behaviour of the system. While the Caputo fractional order derivative offers a promising framework for modelling epidemic dynamics, they often entail significant computational overhead, limiting the scalability and practical utility of fractional calculus-based epidemic models, especially in real-time simulation and forecasting scenarios.

This article introduces a novel approach to data clustering based on the oscillatory chaotic neural network with dipole synaptic connections. The conducted research affirms that the proposed model effectively facilitates the formation of clusters of objects with similar properties due to the use of a slowly decreasing function of the dipole synaptic strength. The studies demonstrate that the degree of neuron synchronization in networks with dipole synaptic connections surpasses that in networks with Gaussian synaptic connections. The findings also indicate an increase in the interval of the resolution range in the model featuring dipole neurons, underscoring the effectiveness of the proposed method.

The utilization of millimeter waves in 5G technology has led to key differences in the capacities and performance of radio communications. Examining the advantages and challenges of this technology and comparing it with an established technology like 4G can provide a deeper understanding of these changes. Overall, this study conducts examinations to provide the characteristics of 5G and 4G technologies. In this study, the performance of 5G was evaluated and compared to 4G, under fair conditions, by analyzing the effect of increasing the distance of antennas, the number of users, and bandwidth on signal power, delay, throughput, channel quality, and modulation metrics. The analysis demonstrates the superiority of 5G in terms of speed and its ability to support more users compared to 4G. The higher data rates and enhanced capacity of 5G are evident in the results. However, it's worth noting that 4G offers a wider coverage area compared to 5G, making it more suitable for certain scenarios where extended coverage is essential. Additionally, it was observed that 5G signals are more susceptible to noise and obstacles compared to 4G, which can impact signal quality and coverage in certain environments. The presented results suggest that using 5G antennas in geographically limited and densely populated areas, such as rural regions, would be more cost-effective compared to using 4G antennas. This is because fewer antennas are required to serve more users without the need for extensive coverage. Additionally, numerous obstacles in urban areas pose challenges to 5G technology, thus requiring a greater number of antennas to achieve satisfactory accessibility.