A Logic Petri Net Model for Dynamic Multi-Agent Game Decision-Making

 

This study proposes a logical Petri net model to leverage the modeling advantages of Petri nets in handling batch processing and uncertainty in value passing and to integrate relevant game elements from multi-agent game processes for modeling multi-agent decision problems and resolving optimization issues in dynamic multi-agent game decision-making. Firstly, the attributes of each token are defined as rational agents, and utility function values and state probability transition functions are assigned to them. Secondly, decision transitions are introduced, and the triggering of the optimal decision transition is determined based on a comparison of token utility function values, along with an associated algorithm. Finally, a dynamic game emergency business decision-making process for sudden events is modeled and analyzed using the logic game decision Petri net. 

Based on reachable markings, reachable graphs are constructed to analyze the dynamic game process. Algorithms are described for the generation of reachable graphs, and the paper explores how the logic game decision model for sudden events can address dynamic game decision problems, generate optimal emergency plans, and analyze resource conflicts during emergency processes. The effectiveness and superiority of the model in analyzing the emergency business decision-making process for sudden events are validated. A sudden event is an emergency that poses direct risks and impacts human health, life, and property, requiring urgent intervention to prevent further deterioration. These intervention measures are organized into a process, which is typically described in an emergency plan and referred to as the emergency response process. 

In this process, all emergency personnel are dedicated to managing disasters to minimize or avoid the secondary impacts of the disaster. Generating better contingency plans before emergency responses have become an urgent issue to address. The uncertainty of evacuation time during emergencies, and its stochastic analysis was conducted by coupling the uncertainty of fire detection, alarm, and pre-movement with evacuation time.The forecasting model is event-dependent and takes into account many social and environmental elements regarding different sorts of events, such as socio-economic situations and geographical features. This is due to the great range of emergency occurrences, including both natural and man-made ones. The business decision-making process in disaster operations management varies greatly depending on the type of occurrence, taking into account factors like severity, impacted region, population density, and local environment, among others. 

There are many different types of hazards present worldwide. The health of vulnerable people is placed at risk by natural, biological, technological, and sociological dangers, which also have the potential to seriously impair public health. For instance, the authorities in-charge of providing clean water are responsible for the prevention of waterborne illnesses, while law enforcement and road transportation agencies are in charge of reducing traffic accidents. Zoonotic illnesses (diseases spread from animals to people) need coordinated action from the agricultural, environmental, and health sectors. These increases in new or reemerging diseases are attributed to a number of factors, including global warming, low vaccination rates in high-risk and vulnerable populations, growing vaccine resistance and skepticism, rising anti microbial resistance, and expanding coverage, frequency, and speed of international air travel. A professional who develops plans for emergencies, accidents, and other calamities is known as an emergency management director. Directors of emergency management work together with the leadership team of an organization to evaluate possible hazards and create best practices for handling them. Designing emergency procedures and developing preventative actions to lessen the risk of emergency circumstances occurring fall under their purview. Directors of emergency management play a crucial part in ensuring the safety of all employees and equipping staff to act effectively in case of an emergency. Plans for disaster preparation choose appropriate organizational resources, lay down the tasks and roles, establish rules and processes, and plan exercises to increase preparedness for disasters. The effectiveness of the response activities is improved when the needs of populations affected by catastrophes are anticipated. The effectiveness of the response operations is increased by increasing the ability of workers, volunteers, and disaster management teams to deal with crises. Plans could consist of the following: Sites for temporary refuge, and routes for evacuation water and energy sources for emergencies. Additionally, they might talk about stockpile requirements, communication protocols, training plans, chain of command, and training programs. One of the most crucial metrics for gauging the effectiveness of an evacuation is the time it takes. 

Residents who are detained for an extended period of time represent a serious threat to staff safety because of the unpredictability of events. A building’s inhabitants who attempt to flee during a fire accident exhibit a range of response times (RTs) between the time they are given a warning and the decision to leave. A number of complex factors, such as occupants’ familiarity with evacuation routes, their ability to operate evacuation amenities and fire protection apparatuses, the number of people in the area,and occupants’ psychological and physical conditions and behaviors, can affect how affected personnel are evacuated from a disaster site. Different factors have an impact on evacuation time (ET). The results indicate that it is a variable influenced by a significant number of uncertain factors, including emergency evolution dynamics, human behavior under emergency conditions, and the environment. The benefits of developing appropriate emergency response plans using safety and industrial hygiene resources to mitigate or prevent harm to factory personnel and nearby community residents caused by chlorine gas leaks. Everyone on the team has to be knowledgeable on how to spot leaks and react to them in order to keep the employees safe when handling chlorine. Since chlorine has a strong, unpleasant scent that resembles that of a potent cleaning solution like bleach, most chlorine leaks are quite easy to detect. Every facility that works with chlorine has to have an emergency kit on hand. This kit should include a variety of tools that may be used to stop or limit leaks around plugs, valves, or the side wall of a tank or cylinder used to store chlorine. Breathe in some fresh air and leave the location where the chlorine gas was emitted. If the community has an emergency notification system, be sure they are familiar with it. For directions, consult local authorities and emergency bulletins. If the chlorine discharge occurred outside, seek protection inside. 

To ensure that the contamination does not enter, make sure all windows are closed and ventilation systems are off. Leave the location where the chlorine was discharged if you are unable to get inside. Get outside and look for higher ground if the chlorine discharge occurred indoors. Open the windows and doors to the outdoors if the chlorine leak was caused by chemicals or home cleaners to allow infresh air. We focus on agent-based problem-solving strategies with business decision-making capabilities for CSC, which are based on Multi-criteria business decision-making methods (MCDM) methods for dealing with automated selection in CSC and PN techniques for modeling such context. Petri nets are used as modeling tools in the discrete-event dynamic process known as the multi-agent system. In comparison to alternating current micro grids, direct current micro-grids stand out for their ease of control and power management. They also offer a number of benefits, including higher conversion and transmission efficiency, greater reliability even in re-mote locations, convenient control, lower costs, and less filter effort due to the absence of reactive power, phase synchronization, high inrush current, etc. A rational actor must interact if enhancing subjective utility necessitates interaction with other agents. If there is contact between rational agents, at least one of the agents is trying to maximize his utility. Agents collaborate if their aims are the same. If their aims conflict, they engage in competition. 

The majority of these interactions occur between these two extremes. An interacting agent would do well to predict the objectives of other agents. A more well-informed actor may foresee some aspects of how other agents will act in response to their objectives. In these situations, strategic thinking is required. A contact in which strategic thinking occurs is referred to as a strategic interaction (SI). In game theory, SI or games are examined. The game theory takes into account reason and the potential to forecast rational behavior. The existence of widespread awareness of reason is assumed. This implies that each participant in an interaction believes in there a son of the others and that they, in turn, believe in his rationality, and so on.The equilibrium is the expected behavior of players or participants in an interaction. If one of the players strays from equilibrium, nobody wins. Because of this, it is termed equilibrium. In finite games, there is at least one equilibrium. At least two application agent and mechanism designs are required for artificial intelligence games. We have a game in agent design and must calculate appropriate behavior. We have an expectation about the behavior and must develop game rules in mechanism design. These two goals can be addressed theoretically by running algorithms over a game tree, or practically by creating an environment in which various real players can interact. Most games are written in low-level programming. Game rules are more easily editable. Algorithms may be created that change game representation in every way imaginable, such as ‘reduce number of players’ or ‘remove simultaneous turns’. 

Game representations may also be used to create evolutionary mechanisms. Logical Petri nets can further simplify the network structure of real-time system models, making it easier for us to analyze the properties of the system at a conceptual level, while also alleviating the problem of state space explosion to some extent. Petri nets can not only characterize the structure of a system but also describe its dynamic behavior. Currently, many scholars have proposed extended forms of Petri nets, such as logical Petri nets, timed Petri nets, and colored Petri nets, and their applications are becoming increasingly widespread. Multi-agent games involve multiple elements, such as players, strategies, utilities, and information equilibrium. The existing modeling elements of logical Petri nets cannot accurately describe these elements, so improvements need to be made to logical Petri nets. Based on the existing modeling elements of logical Petri nets, modifications or additions of new modeling elements are needed to model game elements, enabling the new model to accurately describe dynamic game problems in multi-agent systems. 

We consider a mean-field game (MFG)-like scenario where a large number of agents must select between a set of various potential target destinations. This scenario is inspired by effective biological collective decision mechanisms such as the collective navigation of fish schools and honey bees searching for a new colony. The mean trajectory of all agents represents how each person impacts and is impacted by the group’s choice. The model can be seen as a stylized representation of opinion crystallization in a political campaign, for instance. The initial spatial position of the agents determines their biases initially, and then in a later generalization of the model, a combination of starting position and a priori individual preference. The existence criteria for the specified fixed point-based finite population equilibrium conditions are developed. In general, there may be several equilibria, and for the agents to compute them properly, they need to be aware of all the beginning circumstances.

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Spatial federated learning and blockchain-based 5G communication model for hiding confidential information

 

At present, the preferred method of transmitting a rapid blockchain message is to send several transactions, constituting a covert 5G communication technique. However, this approach is inadequate for processing larger quantities of sensitive data, and the potential for losing confidential information is significant. Additionally, the sender’s identity is not concealed. Despite the high embedding rate of steganography techniques, they are increasingly vulnerable to detection and statistical feature-based analysis. This investigation suggests a covert blockchain communication methodology that incorporates spatial federated learning and spatial blockchain as a means of fixing these issues. By utilizing Ciphertext-Policy Attribute-Based Encryption (CP-ABE) to encrypt the sensitive document and uploading it to the Inter Planetary File System (IPFS), the technique conceals sensitive files and the sender’s identity. Then, using image steganography based on Generative Adversarial Networks (GAN), the sender implants the hash value of the encrypted document into a carrier image. After uploading the encrypted image to IPFS, the sender creates a transaction with the hash value of the encrypted image. This transaction is then signed by a ring signature and broadcasted to the blockchain network for verification and confirmation. The recipient retrieves the encrypted document and decrypts it according to the access control policy established by CP-ABE. According to experimental findings, this model can increase the volume of sensitive data transmitted from KB to MB while providing higher confidentiality and security.

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Exploring the Profound Influence of Machine Learning on Business Intelligence: A Comprehensive Review

In the dynamic landscape of data-driven decision-making, the intersection of Machine Learning (ML) and Business Intelligence (BI) has become a pivotal arena, propelling organizations toward more informed and strategic insights. The fusion of these two domains is characterized by a continuous evolution, marked by innovative trends that redefine how businesses extract value from their data. This synergy between ML and BI not only augments analytical capabilities but also transforms raw data into actionable intelligence, empowering organizations to navigate the complexities of the modern business environment. 

As we delve into the emerging trends in ML and BI integration, it is evident that the convergence of advanced analytics and business intelligence is ushering in a new era of efficiency, automation, and foresight. From augmented analytics and predictive modeling to the democratization of machine learning through automation tools, the landscape is evolving rapidly. This exploration will delve into key trends shaping this amalgamation, offering a glimpse into the future of data-driven decision-making where insights are not just discovered but dynamically generated, enabling businesses to stay ahead of the curve and make strategic decisions with unparalleled precision. 

The preponderance of technology is focused on the creation of value in businesses. Technology is a tool that creates value, and companies exist to facilitate the exchange of value between people. Technology is a tool that allows businesses to trade values more effectively and efficiently and create new values that may be shared, as explained above. Technology has a significant impact since it is continuously developing. Several areas for different sectors enhance how they work, especially machine learning, which helps businesses enhance their business process. Machine learning helps businesses make decisions as it has a strong relationship with business decision-making. 

The contribution of machine learning in companies is essential since it has a strong relationship with business intelligence and helps organizations make better decisions when it comes to decision-making. Without machine learning, business intelligence is ineffective indecision-making, and company leaders cannot make successful decisions without machine learning.Business intelligence (BI) is referred to as converting data into information, subsequently transformed into knowledge. When it comes to business intelligence (BI), the goal is to make better, more informed choices. Business intelligence assists businesses in collecting and analyzing data to detect trends and patterns. This information may then be utilized to enhance strategic planning, operational efficiency, and marketing initiatives, among other things. One of the most significant advantages of business intelligence is that it may assist firms in reducing waste and optimizing resources. An organization that determines that it is selling things that are not in great demand, for example, might change its inventory levels to reflect this information. Alternatively, suppose a company notices that a particular product is being returned at a higher rate than others. 

In that case, it may look into what could be causing the issue and take appropriate action. Organizations may also benefit from business intelligence in terms of improving customer service. Businesses may better know what consumers are searching for by watching their activity over time and analyzing the data. If a firm notices that its consumers are unhappy with its service, it may rectify the situation and improve customer satisfaction. Business intelligence (BI) is now critical component of many firms' day-to-day operations. Businesses benefit from it because it improves decision-making and helps them better understand their goods and services. The better fulfilling consumer wants, increasing sales, providing better service to customers, lowering expenses, maximizing resources, and minimizing waste improve firms' bottom lines. Recently, we've observed integrating machine learning capabilities into business intelligence systems, making BI considerably more successful at uncovering hidden insights. BI solutions that can efficiently combine these skills in a user-friendly manner will soon become the standard. As consumers get used to this feature, they will expect it to be available at all times. 

GPS and other technology that we now can't fathom our lives without are examples of this. Combining these capabilities automates the process of unearthing insights that business users were not aware were available until they were discovered. For example, on a typical dashboard, a business user looking at their top-line sales would conclude that the trend seems to be in good shape and that there is no need to investigate deeper. However, there may be grounds for worry in the fine print, in the underlying makeup of the sales figures, which is difficult to discern. Some items may be doing well, while others may be exhibiting a deterioration in performance. This critical understanding is concealed from public view. Additionally, automation of this process results in insights being supplied much more rapidly, enabling the company to respond quickly and with better information. It allows the business to act faster and with better information. 

Automating these procedures should allow the analyst to spend more time on other responsibilities in their organizations. Many analysts are engaged in regular chores such as variance analysis, the search for anomalies, and the creation of comments for inclusion in reports, among other things. The analyst will devote more time to higher-value activities.In data analysis, machine learning models successfully uncover hidden patterns and insights. For many decades, data professionals have used these strategies to tackle technical and challenging business challenges. Because of improvements in computing power, it is now possible to construct and execute these complicated mathematical models on a more accessible platform. Models that used to need costly, high-end technology can now run on commodity platforms accessible to everyone, regardless of their financial situation. 

Machine models are categorized into Supervised, Unsupervised, Semi-supervised Learning, and Reinforcement Learning models, and these models have several algorithms which can be used for Business intelligence (BI) such as Feedforward Neural Network (FNN), Artificial Neural Networks (ANN), Support Vector Machine (SVM) algorithm, KNN algorithm, etc. This paper will perform a comprehensive review of machine learning models used in business intelligence. Furthermore, we will review the impact of machine learning on business intelligence.

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An Efficient Machine Learning Prediction Method for Vehicle Detection: Data Analytics Framework

 

The availability of transportation is considered a significant hallmark of a developed society. Since the evolution of the human species, the imperative to relocate from one location to another has been a fundamental requirement. At present, there exists a plethora of transportation options in Indonesia. However, most individuals favor road transportation due to its ease and convenience. The rise in population has led to a corresponding increase in the number of vehicles on the roadways. Hence, it presents a challenge for security authorities and governmental bodies to oversee all automobiles' mobility across various locations effectively. 

The present study proposes a methodology for detecting and tracking vehicles using video-based techniques. The process's initial stages involve preprocessing, including frame conversion and background subtraction. Next, the process of detecting vehicles involves the utilization of change detection and a model of body shape. Subsequently, the next stage entails the feature extraction process, focusing on extracting energy features and directional cosine. Subsequently, a technique for optimizing data is employed on the vector comprising excessively extracted features. The methodology integrates a data mining technique based on association rules, which is subsequently complemented by a random forest classification algorithm. The approach generally integrates multiple methodologies to attain effective and precise identification of automobiles in video-derived datasets.

Traffic disruption is a prevalent issue in Indonesia, particularly in the province of Special Capital District (DKI) Jakarta. The authorities have implemented multiple measures to mitigate traffic disruption in Jakarta. One of these initiatives involves the establishment of the Jakarta Smart City information system. The Jakarta Smart City information system harnesses closed-circuit television (CCTV) data from multiple sources, such as the Transportation Agency (DisHub), Bali Tower, the Public Works Service (PU), and Transjakarta, among others. Around 6,000 CCTVs are distributed across the Jakarta region, with their real-time data being transmitted and displayed on the portal of the Jakarta Smart City system. Quick detection of vehicles becomes necessary to provide inattentive drivers with sufficient time to avoid traveling conflicts and thus minimize the likelihood of rear-end collisions. Moreover, the current techniques for traffic surveillance that count automobiles using electric circuits on the road are costly. All of these factors necessitate the investigation of novel and favored techniques for the vehicle recognition task. Typically, the primary objective of detecting vehicles is to identify potential vehicle positions within an image and designate them as areas of interest (A.O.I.) for subsequent processing tasks. In contrast, computerized automobile identification is a complicated and intrinsically tricky task.

To detect moving vehicles on avenues, reliable systems and programs with efficient extraction methods are required. Real-time traffic inputs produce an enormous volume of data every day; to manage such a large quantity of data, artificial intelligence (A.I.) and computer vision methods are combined to improve the precision of the framework. This recent technological advancement has reduced human and labor needs. A robust video-based surveillance apparatus must be adaptable to the environment's behaviors. However, threats such as trembling cameras and noise interference still exist. Recognizing vehicles during the day is difficult because lengthy reflections cast by the sun can lead to misclassification or interference. In contrast, night vision detection presents difficulties due to the lack of adequate enlightenment, making it difficult for the classifier to identify effectively. Identifying target motion using artificial intelligence (A.I.) technology is one of the foundations of automobile environment sensing. Moving objects in conveyance typically refer to automobiles or individuals available in operating conditions. Additional immobile things, including transportation systems and vegetation, are typically called landscapes. 

To obtain the desired format, it is necessary to distinguish moving components from the background contemporaneously by examining the video input footage extensively. Diverse strategies were employed to establish technologies capable of detecting, counting, and classifying automobiles for use in automated transport platforms' traffic tracking. This section addresses the subject matter of these kinds of systems and an understanding of the methodologies used in creating them. Naz et al. presented a video-based actual time tracking of vehicles using the optimized simulated loop methodology. The researchers utilized real-time traffic monitoring equipment installed along roads to determine the number of vehicles that traveled on the road. In this approach, accounting is done in three stages by monitoring the vehicle's movements throughout an imaginary loop monitoring zone. Ukani et al. presented an alternative video-based vehicle identification approach. In this approach, comparatively high-mounted observation cameras were employed for collecting a roadway video feed; the Adjustable framework estimating, and the Gaussian shadowing reduction consisted of the two primary techniques used. The system's precision depends on the viewing angle and its capacity to eliminate shadowing and phantom effects.

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Streamlining Stock Price Analysis: HadoopEcosystem for Machine Learning Models and BigData Analytics

The rapid growth of data in various industries has led to the emergence of big data analytics as a vital component for extracting valuable insights and making informed decisions. However, analyzing such massive volumes of data poses significant challenges in terms of storage, processing, and analysis. In this context, the Hadoop ecosystem has gained substantial attention due to its ability to handle large-scale data processing and storage. Additionally, integrating machine learning models within this ecosystem allows for advanced analytics and predictive modeling. This article explores the potential of leveraging the Hadoop ecosystem to enhance big data analytics through the construction of machine learning models and the implementation of efficient data warehousing techniques. The proposed approach of optimizing stock price by constructing machine learning models and data warehousing empowers organizations to derive meaningful insights, optimize data processing, and make data-driven decisions efficiently. The proliferation of data has transformed the way organizations operate. The ability to extract valuable insights from vast amounts of data has become a competitive advantage across industries. However, traditional data processing and analysis techniques are insufficient to handle the sheer volume, velocity, and variety of big data.
This necessitates the adoption of advanced technologies and frameworks, such as the Hadoop ecosystem, to overcome these challenges. In recent years, the prevalence of big data technology has revolutionized numerous industries, including retail, manufacturing, healthcare, and finance.The utilization of big data has proven instrumental in enhancing operational efficiency by harnessing valuable insights derived from data analysis. This research paper focuses on investigating the application of big data analytics in the context of the stock market, utilizing a publicly available dataset sourced from The New York Stock Exchange (NYSE). By leveraging big data analysis, organizations can identify trends, patterns, and correlations that enable informed decision-making processes. Particularly in the stock market, analysis plays a pivotal role for investors and traders in assessing a company's intrinsic value before executing buying or selling decisions.
The widespread adoption and efficacy of big data technology is largely attributable to the evolution of multifarious frameworks and platforms that cater to the manipulation and scrutiny of colossal data sets. Apache Hadoop takes a preeminent position among these big data platforms, ingeniously amalgamating the powerful MapReduce paradigm and the durable Hadoop Distributed File System (HDFS) for proficient data governance. This technology has been embraced ubiquitously across a myriad of sectors, empowering organizations to distil pertinent insights, thus refining their decision-making apparatus. A case in point is the New York Stock Exchange (NYSE) that has judiciously harnessed big data technology, with a particular emphasis on Apache Hadoop, to conduct in-depth analysis of market fluctuations and draw data-oriented verdicts, conferring upon them a competitive superiority. In parallel, Apache Spark has emerged on the scene as a sought-after big data framework, renowned for its expedited processing velocity and its superior versatility in handling data, thereby outpacing the capabilities of its counterpart, Apache Hadoop.The New York Stock Exchange (NYSE) can harness the capabilities of Apache Hadoop’s MapReduce and Apache Spark frameworks to process and decipher vast quantities of financial data. As illustrated in Table 1, Spark offers superior processing speed and enhanced flexibility in data manipulation, rendering it a prime candidate for processing and analyzing real-time data pertinent to the financial sector, more specifically, within the ambit of stock exchanges. This proves particularly valuable in the dynamic realm of finance where instantaneous data insights are paramount to the decision-making process. In addition, Spark's fundamental component, the Resilient Distributed Dataset (RDD), presents an advantageous data processing approach within distributed systems, exhibiting higher efficiency and fault tolerance compared to MapReduce.RDD programming can be employed for data transformations, including mapping and filtering, as well as operations like counting and collecting. Given its ability to be cached in-memory, RDD enhances data access efficiency. Consequently, Spark can confer a competitive edge to stock exchanges, such as the NYSE, requiring the capability to process and dissect voluminous real-time financial data in order to maintain their standing in the brisk-paced financial industry.

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Support Vector Machine for Multiclass Classification of Redundant Instances

 In recent years, support vector machine has become one of the most important classification techniques in pattern recognition, machine learning, and data mining due to its superior classification effect and solid theoretical base. 

However, its training time will increase dramatically as the number of samples increases, and training will become more sophisticated when dealing with problems involving multiple classifications. A quick training data reduction approach MOIS appropriate for multi-classification tasks is presented as a solution for the aforementioned issues. While eliminating redundant training samples, the boundary samples that play a vital role are chosen in order to considerably reduce training data and the problem of unequal distribution between categories. 

The experimental results demonstrate that MOIS may maintain or even improve the classification performance of support vector machines while substantially enhancing training efficiency. On the Opt digit dataset, the suggested method improves classification accuracy from 98.94% to 99.05%, while training time is reduced to 15% of the original; in HCL2000, the proposed method improves classification accuracy from 98.94% to 99.05%. When the accuracy rate is marginally increased (from 99.29% to 99.30%) on the first 100 categories dataset, the training time is dramatically reduced to less than 6% of the original. Additionally, MOIS has a high operational efficiency.

 

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