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VANET is a critical and demanding mission. Numerous methods exist, but none profits in a distributed fashion from physical layer parameters. This paper describes a method that enables individual nodes to estimate node density, independent of beacon messages, and other infrastructure-based information, of their surrounding network. In this paper, a discrete simulator of events was proposed to estimate the average number of simultaneously transmitting nodes, a functional channel model for the VANETs system, and a method of estimating node density. Proposed based on some equations to allow individual nodes to estimate their surrounding node density in real-time Optimized Node Cluster Algorithm with Network Density in which the composition of a cluster is triggered adjacent, these traffic signals is the same and has been predicated mostly on the position a vehicle might well take after crossing. Additional Ordered Tracking with Particle -Filter Routing in which receives simultaneous signal intensity versus node transmission and node density transmission. Conduct multiple location-related analyzes to test the plausibility of the neighboring single-hop nodes on mobility data. The system is designed to operate in the most complex situations where nodes have little knowledge of network topology and the results, therefore, indicate that the system is fairly robust and accurate.

User data protection is a major problem in the technical world. To get critical data from people such as bank account, credit card details and passwords, fraudulent people use different techniques for their advantages. Misuse of user credentials affects many industry sectors day by day. The fraudulent practice where more people connected to a network revealing sensitive data of individual, group or a company of legitimate sources called phishing. The aids for the hackers are forged websites and emails which look similar as original. There are different anti-phishing methods are proposed which follows various methods, to protect users against phishing. Proposed technique has detection of phishing at various stages. It contains incoming mail analysis for its feature as well as source. If mail has link to webpage, that cautious webpage is checked for its legitimateness. It is examined by using two methods. For popular sites, webpage content is compared with content of known legitimate site. The second step is to find whether any cautious site is increasingly dependable on any related pages to calculate relationship strength indirectly or directly associated pages. To protect from phishing attacks, these techniques uses virtual passwords for logging into sites.

Vehicular Ad Hoc Network (VANET) has received better consideration from academic researcher and automobile companies in modern years. Meanwhile, Congestion control remains the major area of research in VANET due to its characteristics such as high mobility, high vehicle density, and unbounded network size, frequent exchange of information, rapidly changing network topology, and lack of central management. Congestion in the VANET broadcast channel may occur, because of number of vehicles in a relatively tiny area raises, the communications channel becomes exceed by beacon message, and event-driven messages. Researchers have suggested a number of answers to control these issues and also to reduce congestion in VANET atmosphere. Congestion in VANET occurs when the vehicles are in the dense part while the network node is carrying more data than it can handle. More vehicles in cluster area are attempting to broadcast simultaneously in dense condition, it will reduce Packet Delivery Ratio and thus congestion will occur. We proposed density based dynamic clustering (DBDC) which finds the high dense region in the cluster and do sub-clustering. First, we find Average Vehicle Density Threshold Value using Received Signal Strength (RSS) to determine the high dense part. Performance result shows our approach is better in all aspects comparing with VWCA clustering approach and also provides high cluster stability.

this paper discusses how machine learning methods can improve the complexity management of customer attrition networks. User turnover is insufficient in hybrid cellular networks because losses causing delays and failures due to communication errors are handled. As a result, this research work attempts towards automating a loss of classifier's construction from a database created by random network topology simulations using machine learning techniques. This classifier must fulfil both a computational and a time-varying restriction on its misclassification rate on congestion losses to be useful in this application. With these two constraints in mind, multiple machine learning algorithms are compared. Decision tree boosting tends to be the best option for this application. It surpasses the suggested ad hoc descriptive statistics also in networking research. When combined with customer churn, it increases bandwidth usage dramatically over wireless networks while maintaining good customer churn behaviour over wired networks. This research demonstrates the interest in using computer networks; algorithms are used to build protocols.

India is a farming nation, and its financial system is supported by lead harvesting and rainwater. Rainfall Prediction (RP) is a need and essential for every farmer to maintain the investigation of harvest production. RP is the goal of knowledge and proficiency in measuring the status of the environment. Accurate determination of rainfall is crucial for the successful use of water assets, harvesting, and pre-growth of water construction. Rainfall is the key to every action plan in the food production program, water supply system and environment. One of the most difficult responsibilities is to forecast an RP for the weather predictor. RP plays a significant task in farming and business. RP is essential for identifying heavy rains and providing information about normal disaster. Tape occupies a different view of the weather, such as the RP storm path, storm speed, humidity, rainfall prediction, and temperature. RP is the most systematic and technically demanding problem on earth in the previous century. The weather alterations calculate analyzes the behaviour of the weather for a particular stage. RP is a procedure of weather adjustment everywhere, with specific characteristics like moisture and storms being used to calculate rainwater at the exact locality. Various methods evaluate performances such as accuracy, error, accuracy, recall, F-measurement, RMSE, MAE, and RAE. Based on that assessment, this research study can investigate which processes provide the best RP accuracy.

Dimensionality reduction means obtaining additional training data by supplementing this study with supervised learning to match input variables. A training dataset is used due to a lack of training examples in some domains and avoiding the fitting problem. A Convolution Neural Network (GAN) to augment a classification model for computer vision has shown to improve prediction performance. Using the augmented neural network, this study analyses whether transfer processing within the same GAN will improve prediction performance even further. This proposed a method for assessing the effect of data augmentation in the processing of microscopic charcoal images. Morphological transformations and sub-images were various steps to data augmentation that have been investigated. A post-Inception-v3 framework is also applied to educate a barbecue species classifier using the data available. The best value was observed using the micro methodology, which had an overall precision of 99.36%

Brain tumor detection is a challenging task in medical image processing, and a huge amount of research is happening in this context. Radiologist sets aside a lengthy timespan for identifying and classify brain disorders. Denoising techniques will lessen the noise present in the image. The precise denoising method can valuable in distinguish brain disorder location, grade, size etc. the feature extraction techniques used to extract the features of the image. These features of the image used efficiently identify the issues.

Control and monitoring data is transmitted across ICS networks in critical environments like the smart grid. Cyber attacks on smart grid communication can have disastrous implications for energy generation, delivery, and, ultimately, human life. Traditional smart grid security measures such as firewalls and Intrusion Detection Systems (IDS), which are normally put on the network’s edge, cannot detect internal threats since attacks can originate from both within and outside the network. As a result, we must additionally examine the behaviour of internal ICS communication. ICS traffic has predictable and steady communication patterns due to its nature. Statistical models can be used to describe these tendencies. We can develop a statistical profile of the communication based on the patterns found in regular communication traffic by examining chosen elements of ICS network communication such as packet inter-arrival times. This method is efficient, quick, and simple to use. Statistical-based anomaly detection can detect common security incidents in ICS communication, as our experiments indicate. This research utilizes the Local Outlier Factor (LOF) technique to create a statistical model for anomaly identification using selected network packet properties. The IEC 60870- 5-104 (a.k.a. IEC 104) protocol is used to demonstrate the proof-of-concept.

In the finance sector, time series forecasting has been widely used in situations such as stock prices forecasting and equity market forecasting. In recent years, techniques of computer vision include become increasingly popular in the monetary assumption of periodicity. A hot subject is how to mark information from macroeconomic variables to assess the accuracy rate of computer vision techniques and, as a result, final investment returns. Existing information from macroeconomic variables labelling methods primarily mark data and comparing current data to data from a limited period in the future. Information from a macroeconomic variable, on the other hand, are often quasi, with apparent unpredictability in the brief period. As a result, such tagging methods struggled to identify the time series analysis information’s persistent trend functionality, resulting in a misalignment among their pigeonhole results and actual market inclination. A new pigeonhole approach called “continuous trend labeling” is proposed in this paper to fix the issue. This article presents a novel feature preprocessing tack that avoids taking a gander distortion that plagues continuous data consistency and preprocessing approaches. The principle of constant trend marking and an advanced tagging framework for removing consistent trend characteristics from time-series data were then presented, along with a detailed logical explanation. Experiments on the ensembled model yielded a scoring accuracy of 52 percent, which was close to the Cagliari teams. The results were determined by combining all of the networks' ratings. Even though it appears to be a low percentage, being right on a trade position more than 50% of the time is considered good, particularly when looking at the data in isolation. Our labelling system is significantly superior to classifiers and other identification evaluation measures; province tagging approaches are used. According to the observations of the study, word embeddings like LSTM and GRU are suitable to finance related time series data prediction.

Lung tumor is a complex disease that occurs due to the abnormal growth of lung cells. For efficient treatment planning, earlier detection of tumor is necessary. Computer-Aided Determination (CAD) can be nearly as viable advertisement twofold perusing by giving a moment conclusion to the radiologist and offer assistance in expanding the affectability and exactness of discovery. The mechanized Division with earlier models or utilizing earlier information is troublesome to actualize. The flawless Division of inside structures of the lung is of incredible eagerness to ponder and treat tumors. The proposed Lung Tumor location calculation comprises four stages: Pre-processing, Division, Include extraction and Classification. Major steps in pre-processing are made strides colour based histogram equalization. A novel calculation for lung MRI division utilizing adjusted colour-based K-mean clustering proposed in this work.

This project evaluates the effectiveness of some DM methods for predicting rainfall using India's historical weather data gathered from the IDM database. Including 10 attributes from the collected meteorological data, only 5 features are generally believed to be significant for precipitation forecasting. The following phases of research are used in the collected data: data cleaning, data selection, Data Transformation and classification. Many pre-processing responsibilities are concerned with determining missing values and eliminating clutter. Hence, two responsibilities are carrying out for finding missing value purpose, which is cleaning and normalization. In this work, six data mining classification methods, including MLR, K-NN, SVM, ANN, Random Forest (RF) and decision tree (DT), were investigated. The classification process divides objects into different classes. A classifier is often trained using a training set, where single or multiple experts assign labels to a set of objects. The proposed RF is a novel classification technique used in rainfall data to classify the region differently from normal rainfall or heavy rainfall. The completed accuracy of the planned RF classifier is 96.1%, which is advanced than other classifiers used in the Rainfall database.

With the advent of artificial intelligence and IoT linkages, human resource management (HRM) is undergoing radical changes and moving onto the new coinage called smart HR. The features of HR in industries are intentional, along with the need to shift to a smarter domain. With regard to industry dynamics, as well as working conditions and the overall organizational structure, the advantages of the technologies implemented and the methodologies suggested have been presented. Also addressed are the industrial shifts relating to the evolving economy and other developments brought about by the introduction of novel systems and technologies. The research also took on the influence of the introduction of new working techniques such as smart work (SW), structural equation modeling (SEM), and smart city projects (SCPs) in the field of smart HR. Several steps taken for the development and growth of the sector were also highlighted, along with recommendations that could be taken up to further boost the experience of work and organization. For a deeper understanding of the principles, a lot of studies were also included, including supply chain systems and cyber-physical systems. Besides, the identification of domains needing further study was also highlighted. In addition, to get the best performance, strategic alliances, line management, and organizational design were addressed too. Focus on the digitization of human resource management was also discussed on the changes of the organizations from e-HRM to d-HRM.

Due to the rapid growth in wireless communications, vehicular ad-hoc networks (VANETs) face many challenges over wireless communication networks. Nowadays VANETs have become a major research and they have incredible resources to improve road traffic efficiency, safety, convenience and comfort to both drivers and passengers etc. Congestion occurs when the vehicles are in the dense part while the network node is carrying more data than it can handle. The proposed DBDC: density based dynamic clustering devices determine node density of the precise location in a lane and provide a proactive solution for congestion. To trigger the congestion control process in the cluster, we use average vehicle density threshold which is calculated using trained dataset. If the node density is greater than the threshold value, the dynamic clustering process is triggered. Our simulation result indicates that the overall performance of our scheme is better than multi agent dynamic clustering and vehicular weighted clustering algorithm in terms of average cluster number, cluster head duration and average cluster member duration.

Developing successful embedded vision applications necessitates a detailed review of various algorithmic optimization trade-offs and a wide variety of hardware design options. This makes it difficult for developers to navigate the solution space and find design points with the best performance trade-offs. In neural machine translation, large Transformer frameworks have produced state-of-the-art results and have become the industry standard. We will clarify the mathematical mechanisms behind the paper's efficient interpretation for neural machine translations and look for the best combination of known techniques to improve interpretation speed without compromising recognition accuracy in this paper. We perform an empirical study that compares various approaches and shows that using a hybrid of decoder self-attention replacement with simplified recurrent units, a deep encoder and shallow decoder architecture, and multiple head attention reseeding can achieve up to higher accuracy. By replacing heavy functions with lighter ones and enhancing the autoencoder's layers structure, excellent results can be achieved in a harmonious mix of time series, network architecture, and probabilistic solutions.

A basic understanding of probability, of its key mathematical features as well as the characteristics it presents within specific circumstances, is the aim of this paper. The action of probability is related to the characteristics of the phenomena that we can forecast. This relation can be described as a distribution of probability. Identify the nature of phenomena (which can also be described by variables), the distribution of probability is defined. The likelihood can be represented by a binomial or Poisson distribution for categorical (or discrete) variables in the majority of cases. For their potential use, distributions of probability are briefly defined along with some examples. The Poisson distribution is a discrete distribution of probabilities that is mostly utilized within a given time span for a model distribution of count data, such as the number of traffic accidents and the number of phone calls received. Each entry starts with a definition and explanation of the Poisson distribution properties, that is followed by a discussion of how to obtain or estimate the Poisson distribution. Finally, every entry provides a discussion of applications that use python libraries for delivery.

Details persistence axioms suggest that almost any based method conducts a normal quest without substitution, as well as an evolutionary algorithm unless it makes fun of concern search goal or lookup burdened. The protection of salt marshes below rapid sea-level rise (SLR) usually includes the survival of underdeveloped moorlands of marsh betrayal zones with which wetlands can transition. Optimal conservation planning of this kind includes details on forest protection's potential benefits and the cost of worthy sites for migrating marshes in specific areas. Although available content is known within the literature on marsh benefits, the prior study offers little visibility into the related costs of land protection. Discrete mathematics shows that a largish task requires the success of issue data. Computers are ineffective to overcome even moderately sized hiccups without reliable information to direct them, considering their pace in performing inquiries. Three tests are proposed to classify the information needed for an effective search: (1) ligneous particulars that measure the difficulty of finding a goal using a random search; (2) physiologic particulars that estimate the challenge of obtaining a goal once a search has some of the problem-relevant information; and (3) active particulars that distinguish the differences among specific particulars. The results show the conservation planning perspective that models can provide and help review simplistic proxies to estimate the cost of conservation of land desirable for marsh migration. This paper establishes a technique focused on these knowledge measures to gauge the efficiency with which efficient search is enabled by dilemma information. This technique is then extended to numerous search instruments commonly used in the evolutionary quest.

the authors suggest possible arrangements between different supermarket chains in this study, using a strategic methodology and game theory to develop a modern, with generations to come, effectively diets. The authors will specifically acknowledge service providers and sellers of vegetarian (vegetarian) but semi (or non-vegetarian) foods. The strategic approach being used by authors offers a model of mathematical game theory that could help vegan food manufacturers get easier entrants and free substantial ads. Besides that, the template could allow non-vegetarian food producers to turn to vegan and vegetarian production seamlessly. In particular, the authors suggest an arrangement between McDonald’s and Muscle of Wheat since they assume that without the aid of a major food supplier already on the market, Wheat’s strength will not reach a global market. The game theory model describes an irregular R&D partnership between McDonald’s and strength of Wheat. Demand for food items that are organic, nutritious, and environmentally friendly has been growing. In response, producers' market natural or other quality-differentiated foods, often claiming to have implemented good habits focused on environmental welfare. These goods have unfalsifiable consistency characteristics, as well. Because the seller may deceive the buyer with a baseless assertion, the manufacturer who maximizes profit can earn a better price with lower production costs. The research addressed in this paper indicates that the availability of high-quality certificates requires regular agreements and third-party oversight. Policy implications of this research are recorded for national organic food criteria.

The Binary classification is the most challenging problem in machine learning. One of the most promising technique to solve this problem is by implementing genetic programming (GP). GP is one of Evolutionary Algorithm (EA) that used to solve problems that humans do not know how to solve it directly. The objectives of this research is to demonstrate the use of genetic programming in this type of problems; that is, other types of techniques are typically used, e.g., regression, artificial neural networks. Genetic programming presents an advantage compared to those techniques, which is that it does not need an a priori definition of its structure. The algorithm evolves automatically until finding a model that best fits a set of training data. Feature engineering was considered to improve the accuracy. In this research, feature transformation and feature creation were implemented. Thus, genetic programming can be considered as an alternative option for the development of intelligent systems mainly in the pattern recognition field.

Technology is the resource of the new economy, and the center of technology administration, the essence of corporate organizations. It was used as a method for generating sustainable economic gains and higher performance from the 1990s. It has now become a key asset to maintain profitable corporate advantages and a catalyst for sustained progress and innovation. Each corporation aims to expand and develop whether it is a small business or a large company so that the owners can return on their investment. This objective can be achieved by sustainably superior corporate efficiency. Several variables will affect the current economy's operational success, but information management is becoming more relevant. This paper is intended to summarize knowledge management, emphasizing the importance of this practice area and, on the other hand, presenting some case studies on how knowledge management from various industries is applied. Therefore, after analyzing many case studies where knowledge management is applied, we will explore the concept that knowledge management has significant consequences for an organization's efficiency. We will continueby emphasizing organizations' general view, their current economy, the information management framework, and how it can be used in organizations. This is accompanied by an overview of information management instances applied and their effect on overall performance. Ultimately, our statement indicates that knowledge management has a positive impact on business efficiency. &nbsp

In this work, we determined the electron dispersing structure factors, just as the vitality levels of certain cores. The computation of electron dispersing structure factors needs numerous issues to be remembered for request to make these figures attainable and quick in time in light of enormous measure of terms speak to arithmetic, quantum mechanical speculations, atomic shell model hypotheses and equations. In the current work, we examined the impacts of the higher setup outside the shell model space and the inactive center which included Tassie Model (TM) to discuss the Longitudinal C2 electron scattering form factors for the nuclei: 116Sn, 92Mo,90Zr ,39K and 32S, which calculated for nuclei under consideration, are compared with those of experimental data. The HO and SKX possibilities have been utilized to compute the wave elements of outspread single-molecule framework components. Some hypothetical vitality levels of the 52Cr, 32S and 181Ta nuclei are calculated compared with their experimental data. The shell model for windows code NuShellX@MSU has been used in this study.This article is being retracted because it contains substantial overlap with a manuscript submitted to Papers in Physics by Shamil Radhi and ultimately not published, under the supervision of Professor Dr. Khalid Salih Jassim at the University of Babylon, College of Education for Pure Sciences. In addition, the name and affiliation of K. Praghash was used without their knowledge or consent. AIP Publishing has a strong commitment to preserving the integrity of the scientific record, and given the extent of the overlap and the failure to properly credit the original source, this article has been retracted on August 16, 2021.

Mobile ad hoc network (MANET) is indeed a series of nodes that have been put with others to collaborate without infrastructure. In this paper first proposes an aggressive forward infinite-dimension...

This work explains about the physic-chemical characteristics of composites to evaluate the applicability in various purposes over agricultural area. Spectral characteristics of compost materials are compute with spectroscopy with Fertility index and clean index. This FI of composites was observed for sampling with various classification of higher fertilizing potential. Composites are utilized as fertilizer owing to low fertilizing potential. The concentration has been determined within permissible limits of fertility standards. It is known that these composites is more appropriate option for waste processing as it diminishes weight of organic waste. This analysis is performed with spectroscopy analysis.