Gongcheng Kexue Yu Jishu/Advanced Engineering Science

In this paper, a mathematical model for Tomato Yellow Curl Virus (TYCLV) with control parameters is established. By using next generation matrix, the basic reproduction number is calculated. The local and the global stabilities are analysed associated with the disease free and endemic equilibriums. The bifurcation of the model is studied. The sensitive indices for the TYCLV of spread and eliminate are determined. The numerical simulation is carried out using MATLAB.

Incorporating Machine Learning (ML) methods into flight instruction is now seen as a game-changing strategy to update aviation curriculum and raise safety benchmarks. In this work, many ramifications of using ML in pilot training, including the opportunities, threats, and ethical issues that may arise has been investigated. The research technique is methodical, consisting of steps like gathering data, cleaning it up, choosing an algorithm, building a model, and testing it. The results were shown using fictitious datasets whose parameters were based on those utilized in aviation. Predicting critical flying circumstances using the Isolation Forest method and evaluating pilot stress using neural network-based stress level prediction were the two key goals pursued. The results demonstrate ML's usefulness in spotting outliers and tailoring approaches to stress management. The ways in which these discoveries might improve aircraft safety, pilot happiness, and realism in training are emphasized in the discussion. The results of the studies are strengthened by the use of theoretical models like the Yerkes-Dodson law. In the publication, the author stress the importance of the research for future pilots, schools, authorities, and business executives. With its potential to reinvent aviation education, redefine operating standards, and eventually contribute to safer skies, machine learning has a place in the training of pilots. This study paves the way for more investigation into such emerging topics as real-time adaptability, extensive biometric integration, simulated reality experiences, and moral concerns.

In recent years, IoT (Internet of Things) devices and their attendant solutions have become more prevalent, particularly in manufacturing, supply chain management, healthcare, transportation, and numerous other "smart" settings. Thanks to IP (Internet Protocol) addresses designated to IoT devices, cyber-physical systems are now able to communicate with one another automatically. Inadequate protection on these ultimate platforms has resulted in attacks such as denial-of-service, Botnets, identity theft, and information theft. Mirai, Torii, Emotet, Dridex, Trickbot, Gluteba, and QBots are examples of Internet of Things-related threats. With the aid of AI, we can at last create cyber-physical systems that are both trustworthy and secure. Machine learning and deep learning approaches combat cyberattacks by identifying and blocking Botnets. This paper investigates Botnet attacks, which are prevalent in IoT devices due to a lack of security requirements during manufacturing or a lack of user security awareness. Anomaly detection is a potential weapon in the hands of machine learning or Deep Learning for identifying and preventing cyberattacks on Internet of Things devices. In this study, we present a Botnet detection system capable of detecting an attack on live traffic. Next, we employ the Aposemat IoT-23 dataset to evaluate and compare a variety of machine learning as well as deep learning techniques for detecting Botnets based on their conventional characteristics. Gated Recurrent Unit (GRU) is a deep learning model with a detection accuracy of 99.87% for identifying Botnets. Thirdly, we use the Wireshark program to investigate the Aposemat IoT-23 dataset's raw packet captured (pcap) files for attacks. Then, we employ GRU, a deep learning model, to identify malware infections with a 99.89% success rate and reduced time complexities.

Video streaming is based on video applications such as videoconferencing, video on demand, Netflix, Amazon Prime, Youtube, Hotstar, etc. In this paper, we are reducing video delay by improving the quality of experience and quality of service by using real-time protocols RTP and HTTP with the help of cognitive network and RLNC algorithm, Without compromising high-quality video. Here we are comparing both the protocol, which is best for video streaming using 3 different videos (MP4, HD720, and HD1080 pixels) with four-parameter matrices (packet delivery ratio, end-to-end delay, throughput, and energy). Simulation results verify the significant improvement achieved by the proposed protocol compared to other existing RTP and HTTP protocols under different network conditions.

This paper propose an eXtreme Gradient Boosted Multifactor Ensemble Relevance Vector Node Classification based Attack Detection (XGBMERVNC-AD) Model with the goal of enhancing the performance of attack detection in larger wireless network with lesser communication overhead,. Boosting is a machine learning ensemble meta-algorithm. Boosting is employed in XGBMERVNC-AD Model in order to further increase the stability and accuracy of node classification during the attack identification process. The XGBMERVNC-AD Model considers multifactor such as energy, loss rate and trust value, and cooperativeness of each nodes in network for accurate attack detection. The XGBMERVNC-AD model generates ‘n’ number of weak Relevance Vector Node Classifier (RVNC) results for each an input sensor node based on observed energy, loss rate and trust, cooperativeness value. After that, the weak classifier’s output is combined into strong classifier by considering their error value. This helps for XGBMERVNC-AD model to boost the accuracy of abnormal behavior identification in large wireless IoT network with a lower time usage. Experimental of XGBMERVNC-AD model is accomplished by taking metrics such as attack detection accuracy, communication overhead and packet delivery ratio with respect to various numbers of input sensor nodes.

Excessive treatment of cardiovascular disease-related health information has led to an increase in a specific order that limits independent analysis for variable estimation in decision-making. A major illness that can be fatal to a person is coronary disease, which is characterized by a heart condition that tends to malfunction. Proper identification during the first phases of the illness can avert cardiac failure, therefore sparing a person's life. Many studies are being conducted to identify heart problems. Improvements are obtained by accurate diagnosis and early identification of health problems through patient information analysis. The proposed structure offers a unique method depending on the blockages of the heart's major blood vessels—the hybrid Improved Multilayer Perceptron approach with Weighed Quantum Dragonfly Algorithms (IMLP-WQDA) to assess the degree of severity of heart disorders. To comprehend the direction of the qualities influencing the outcomes, the study uses principal characteristic assessment. The study employs a method to identify and extract the affecting variables, depending on the UCI information. The ultimate goal of this effort is to identify the criteria that have the most influence on the detection of heart illness and to acquire 36 traits that are primarily grouped. To do this, the IMLP-WQDA model—which uses a variety of characteristics to determine the severity of cardiac disease—is presented. For the best learning environment, the implemented IMLP-WQDA makes use of back-propagation. To comprehend the direction of the qualities influencing the outcomes, the study additionally uses a principal attribute assessment. The analysis of cardiovascular disease severity using the proposed IMLP-WQDA framework is the work's ultimate product. It has been discovered that, in comparison to conventional methods, the evaluation of the proposed approach offers somewhat greater precision and predictive indicators.

Removing the oily effluent from the wastewater is the project's goal. In the industrial sector, pollution has led to numerous issues. Wastewater is made clean by eliminating oil contamination. There are three types of oil skimmers that are commonly found: weir, oleophilic, and non-oleophilic. The feature that separates oleophilic skimmers is the part that collects the oil (rope, disk, belt, or drum), not how they work. Even a thin layer of oil floating on the water can be eliminated by it. The belt's "oleophilic material" is mostly to blame for this. To recover spilled oil from surface water, an endless belt oil skimmer that floats freely was created. The skimmer makes use of a special, highly efficient belt that is motor-driven. We can prepare water for use in other processes by removing oil. This can reduce pollution from oil spills and prevent water waste. Currently, most of the oil produced by industries ends up in rivers, ponds, and the ocean. National and international environmental regulations are therefore becoming more stringent every day. To comply with these standards, it is cost-effective to build a cheap machine.

We study the oscillatory properties of the second order half-linear difference equation Δ(g(t)Δ|u(t)|^(τ-2) Δu(t))+s(t)|Δu(t)|^(τ-2) Δu(t)-h(t)|u(t+1)|^(τ-2) u(t+1)=0, τ>1 (HL) The core ideas of oscillation theory for this equation will be shown to be quite similar to those for the linear equation. Δ(g(t)Δu(t))+s(t)(Δu(t))-h(t)u(t+1)=0. We establish some sufficient conditions related to the oscillatory behavior of the equation(HL). Examples are provided to illustrate the importance of the main results.

In this paper, a mathematical model of Cervical Cancer is formulated. The boundedness and the positiveness of state variables are verified. The equilibrium points are found. The local and global stability of the model is analyzed. Numerical simulations are performed to show the flow the variables.

This paper discuss the implementation of fractional order controllers to large inter connectedpower systems with HVDC link. In recent years researchers paid much attention on application of fractional order controllers to power system stability issues. The enhanced performance of FOPI controllers oner Integer order controllers can further be improved by tuning the controller parameters. In this work improved version of DE algorithm is used to tune the FOPI controller. The effectiveness of the proposed training algorithm is tested on IEEE 9-Bus test power system using MATLAB/SIMULINK under different fault conditions.

As there are number of investment alternates available in the market gold is also one of them. We are testing the risk return relationship of gold in comparison with risk free securities. For this the data of daily gold prices is taken from MCX for the period 2005 till 2018. To analyze the data descriptive statistics is used to check the normality of the data. The risk return comparison is dome with the help of Sharpe ratio analysis. The results concluded that the gold gives higher returns over the period of time but sometimes it becomes risky to invest in such volatile investment alternate.

Clustering is an extremely efficient technique for saving energy in the Wireless Sensor Networks (WSNs). WSN is based on the hierarchical cluster, and hence the Cluster Heads (CHs) will consume some more energy owing to the additional overload for the receiving and the aggregating of data from that of their member sensor nodes, and this transmits all the aggregated data to its Base Station (BS). So the proper selection of the CH has a vital role to play in conserving the energy of the sensor nodes for the purpose of prolonging the WSNs and their lifetime. Here in this work, a proposed Low Energy Adaptive Clustering Hierarchy (LEACH), the Genetic Algorithm (GA) and finally the Chemical Reaction Optimization (CRO) algorithm is considered. The LEACH is a popular clustering algorithm wherein the sensor nodes will elect themselves to be the CH having a certain probability. But the main disadvantage of this algorithm will be that it may choose a CH having low energy that will be able to die quickly and thereby degrades the network performance. So there is a large number of algorithms that are developed for improving the LEACH. The selection of the CH will be a non-deterministic Polynomial (NP)-hard problem. The work has proposed one more energy efficient CH algorithm of selection that has been based on the CRO and the GA algorithms. This type of a CRO algorithm has been developed using some efficient schemes of the encoding of molecular structure that is for the energy efficiency considering several parameters like the intra-cluster based distance, the residual energy and sink distance. In case of the formation of the cluster and its phase several distance and energy parameters have been considered. This algorithm has been extensively tested based on different scenarios of that of the WSNs by means of varying the other sensor nodes and the CHs. These results have been compared with certain currently existing algorithms for being able to demonstrate the proposed algorithm and its superiority.

Recent global events have highlighted the need for autonomous crowd analysis. This technology is gaining interest in computer vision and cognitive science, as well as in the real world, where crowded scenes and their behavior are being increasingly studied. With the rise of crowd phenomena in the real world, crowded scene and their behaviour analysis has gotten a lot of attention recently. When overcrowded towns are subjected to regular crowded events such as strikes, protests, parades, any religious gathering or other types of public gatherings, they face a slew of security challenges. To address these concerns, human security personnel are often deployed to supervise meetings and protect the safety of attendees. However, in crowded spaces such as COVID-19 breakout sessions and public events, it can be challenging to manually manage, count, secure, and track everyone present. An automated system could help to improve safety and efficiency in these situations. Due to significant occlusion, complicated actions, and posture changes, assessing crowd situations is difficult. Crowd analysis is divided into two main categories: crowd statistics and crowd behavior analysis. Crowd statistics determines the level of service (LoS) of a crowded environment, while crowd behavior analysis identifies the mobility patterns and activities of people in a scene. Crowd behaviour analysis has become an essential tool for assuring peaceful event organizing and minimal casualties in public and religious venues across the world. Traditionally, handmade characteristics were used to compute crowd analysis. This paper provides an overview of contemporary exploration study of various crowd behaviour analysis approaches and address new unexplored problem with the eye of deep learning.

Chronic diseases have emerged as a major global public health concern, with their incidence increasing at an alarming rate. Early detection and prevention of these conditions not only improves patient outcomes but also reduces healthcare costs. Leveraging the synergy between Machine Learning (ML) and the Internet of Things (IoT) has enormous potential to revolutionize healthcare, notably through predictive modelling for chronic illness risk assessment. This analysis digs into the challenges and potential involved in developing a prediction model for chronic disease using ML and IoT. It provides a complete overview of current research status and outlines critical routes for future inquiry, addressing topics such as data protection, algorithm refining, and seamless interaction with electronic health records. Furthermore, the significance of wearable gadgets and remote monitoring in forecasting chronic diseases is investigated, as is the possibility of precision medicine for tailored risk assessment. This paper emphasizes the significance of interdisciplinary collaboration and standardization in enabling the effective deployment of ML and IoT-based solutions for chronic disease prediction.

Self-Compacting Concrete (SCC) is known for its higher paste volume, which can lead to increased cracking and a higher creep coefficient, making it brittle. The construction industry must reject such fragility in concrete. An effective method to improve the concrete's flexural and ductility properties is by incorporating fibres. These fibres significantly enhance its capacity for absorption energy and reduce the occurrence of cracking. Three specimens were tested for each case. Nylon fiber has shown better acceptance test results followed by Synthetic fiber when results are compared. To accomplish the study's goals, every fibre was added to concrete mixtures in the following proportions: 0.5 per cent, 1 per cent, 1.5 per cent, and 2% by the weight of cement.

Steganography and irrigation are key areas of the indirect areas of information. Combining the secret messages in digital audio is more complex than the other standard media, such as digital images, the Human Audit System (HAS), multimedia data hiding techniques, digital rights management, secret communications, access phase for controlling the masking process.All the above applications and multimedia steganography techniques should meet two essential requirements such as cognitive transparency and stego subject. The secondary control rate is the high data rate of embedded data. All stego-applications require a higher bit rate of embedded data, but there is a need for diagnostic and encoding algorithms without access to the original multimedia display. The LSB coding is an early technique examined in the area where the digital audio information is hidden and in the drowning area. The main advantage of the LSB encoding system is the overwhelming majority of the Watermark Channel Bit Ratio and Algorithm is a low computational problem that has substantially less strength against major defective signal processing changes. The work mainly focuses on a method that hides the way the viewer suspects it. If anyone knows that there is still data in the audio, it is very difficult to extract the host audio data. The accuracy of detecting hidden information in MP3 files drops with the influence of the compression rate or increases. This drop or increase is caused by either the increase in file track size, the sampling rate or the bit rate. This paper presents an experimental study that evaluates the detection accuracy of the secret data embedded in MP3. Training data were used for the embedding and detection of text messages in MP3 files. Several iterations were evaluated. The experimental results show that the used approach was effective in detecting the embedded data in MP3 files. An accuracy rate of 97.92% was recorded when detecting secret data in MP3 files under 128-kbps compression. This result outperformed the previous research work.

Despite the growing reliance on Healthcare 5.0 due to the COVID-19 epidemic, current solutions are not immune to cyberattacks. This research work proposes a community cloud-based Secure Healthcare 5.0 (CCSH) system that ensures security at the user level, mobile application integrity, hardware or device integrity, and communication security. Utilizing Risk Impact Parameters (RIP) and Threat Events for risk calculation, this study presents a Healthcare 5.0 system that is both safe and robust. For security, we employ UICC (Universal Integrated Circuit Card) and TPM (Trusted Platform Module), and for quantitative and qualitative risk assessments, we use the PILAR Risk Management Tool. In addition to these, our proposed CCSH system ensures security on the cloud side and ensures forensic readiness. The proposed protocol has lower computational and energy costs. The CCSH protocol is verified using Burrows-Abadi-Needham (BAN) logic.

The gudgeon pin is one of the engine's most stressed components. Gudgeon pins are used in automotive engines to link the piston and connecting rod. This study looks at the detailed design and analysis of existing Gudgeon pins in order to detect the pin's stress concentration and then minimise it by making necessary changes to the pin's design and mounting. The current work involved constructing a Gudgeon pin and then using Finite Element Analysis (FEA) software to examine and minimise stresses on the pin. They chose silicon nitride as the best material after comparing titanium alloy, nickel alloy, and silicon nitride.