Process mining enables organizations to discover, monitor, and analyze their work processes based on data. A fundamental requirement for initiating a process mining project is the availability of an event log, which is not always readily available. In such cases, extracting an event log typically involves various time-consuming tasks, such as writing custom structured query language (SQL) scripts to extract relevant data into an event log format from a relational database. In this work, we explore the potential of large language models (LLMs) to support event log extraction for process mining by leveraging LLMs' ability to produce SQL scripts. We evaluate the effectiveness of LLMs in assisting this process and analyze their performance across a range of scenarios. Despite the inherent non-determinism of LLM outputs, our findings highlight the potential of future LLM-assisted tools in automating event log extraction, particularly when provided with the appropriate domain and data knowledge context. The implementation of such tools could democratize access to process mining by reducing the need for specialized technical expertise for producing relational database query scripts and minimizing manual effort.
Effective business process execution requires the integration of both process logic and business data. While recent approaches explore the potential of Large Language Models (LLMs) in automating process modeling, their applicability is limited in real-world scenarios where textual descriptions - often authored by non-experts - are complex or incomplete. Moreover, these works primarily focus on the control-flow perspective and overlook the critical role of data modeling and execution. In this paper, we propose a hybrid and decomposed approach to automatically generate executable process and data models from text using LLMs. Our method modularizes the task: the LLM clarifies and enriches the description, then extracts both process and data elements, which are combined into a unified model. Structured algorithms ensure robust and executable outputs. Evaluation results demonstrate that our approach improves model completeness, clarity, and efficiency compared to existing methods.
Predictive process monitoring (PPM) focuses on predicting future process trajectories, including next activity predictions. This is crucial in dynamic environments where processes change or face uncertainty. However, current frameworks often assume a static environment, overlooking dynamic characteristics and concept drifts. This results in catastrophic forgetting, where training while focusing merely on new data distribution negatively impacts the performance on previously learned data distributions. Continual learning addresses, among others, the challenges related to mitigating catastrophic forgetting. This paper proposes a novel approach called Continual Next Activity Prediction with Prompts (CNAPwP), which adapts the DualPrompt algorithm for next activity prediction to improve accuracy and adaptability while mitigating catastrophic forgetting. We introduce new datasets with recurring concept drifts, alongside a task-specific forgetting metric that measures the prediction accuracy gap between initial occurrence and subsequent task occurrences. Extensive testing on three synthetic and two real-world datasets representing several setups of recurrent drifts shows that CNAPwP achieves SOTA or competitive results compared to five baselines, demonstrating its potential applicability in real-world scenarios. An open-source implementation of our method, together with the datasets and results, is available at: https://github.com/SvStraten/CNAPwP.
With the recent success of large language models (LLMs), the idea of AI-augmented Business Process Management systems is becoming more feasible. One of their essential characteristics is the ability to be conversationally actionable, allowing humans to interact with the LLM effectively to perform crucial process life cycle tasks such as process model design and redesign. However, most current research focuses on single-prompt execution and evaluation of results, rather than on continuous interaction between the user and the LLM. In this work, we aim to explore the feasibility of using LLMs to empower domain experts in the creation and redesign of process models in an iterative and effective way. The proposed conversational process model redesign (CPMR) approach receives as input a process model and a redesign request by the user in natural language. Instead of just letting the LLM make changes, the LLM is employed to (a) identify process change patterns from literature, (b) re-phrase the change request to be aligned with an expected wording for the identified pattern (i.e., the meaning), and then to (c) apply the meaning of the change to the process model. This multi-step approach allows for explainable and reproducible changes. In order to ensure the feasibility of the CPMR approach, and to find out how well the patterns from literature can be handled by the LLM, we perform an extensive evaluation, also in comparison to a baseline approach without change patterns. The results show that some patterns are hard to understand by LLMs and by users and that clear change descriptions by users are essential. Overall, we recommend a hybrid approach that identifies all used change patterns and then directly applies those patterns that work correctly and for the others derives follow-up questions in order to improve user input.
This work introduces a novel advancement to edge computing by introducing a multi-objective optimization approach. The primary objective of this study is to address the existing research challenges associated with integrating edge computing and Internet of Things (IoT) devices. The utilization of an artificial bee colony technique has led to a decrease in both response time and energy usage within edge computing environments. A formulation of an optimization algorithm based on edges is proposed in order to effectively optimize the trade-off between response time and energy cost. The proposed method exhibits encouraging outcomes through meticulous evaluation in substantially decreasing response time and enhancing energy efficiency. This pioneering approach highlights the potential of the artificial bee colony algorithm as a robust algorithm for enhancing the performance of collaborative edge computing systems.
From a Licensed Medical Practitioner's (LMP) perspective, e-Healthcare Risk Prediction plays a vital role in Health Big Data. This also is a hot issue in e-healthcare because of the lack of security and privacy protections. To overcome this deficiency, this research article proposes heterogeneous network systems (HNS), an efficient and privacy-preserving e-Healthcare Risk Prediction method for e-healthcare. In comparison to the existing research contribution, the proposed HNS accomplish two steps of disease risk prediction, namely Analysis of HNS, and Heterogeneous Network (HetNet) concerning the LMP for analyzing the in-hospital involvement care by collecting and explaining the "Health Big Data" as per the view of the LMP. This will help to access the services from the hospital. In the LMP-Centric Heterogeneous Network Powered Efficient e-Healthcare Risk Prediction phase, the "Polygenic Score" is calculated for risk prediction for health big data. Through the characteristics of "non-predictive applications" and "Predictive applications," procedural aspects are analyzed with the LMP-Centric HetNet against the Efficient e-Healthcare Risk Prediction. This will be applied to the Medical extensive data integration and clustering for handling Health Big Data. Finally, the LMP-Centric HetNet Powered Efficient e-Healthcare Risk Prediction for Health Big Data treats the LMP perspective efficiently. The proposed system increased prediction accuracy to 45.9%, and the monogenic score increased from 3% to 19%. The density accuracy range is increased from 13.9% to 39%. The increased execution time is improved from 29.95% to 36.05%. This comprehensive prediction analysis accuracy range is 73.98% efficient.
To address the issues of lengthy modeling time and substantial result error while determining the content of each component in a multi-component system simultaneously using spectrophotometry, a new determination method is provided. If we imagine the test light as a one-dimensional optical quantum and the multi-component system as a one-dimensional square potential barrier, then the process of simultaneously determining the content of each component by spectrophotometer is the process of photon tunneling through the potential barrier of multi-component system; the potential energy matrix of the multi-component system is established and transformed into a Jordan matrix analogous to it. Light quantum tunneling across a barrier in a system with several components is the superposition of light quantum tunneling through each individual component systems, with each component system serving as the “diagonal” in a Jordan block. For a multi-component system, the concentration ratio of each component can be calculated by determining the weight coefficient of the transmission wave of each single molecule system inside the total transmission wave using the multi-core learning (MKL) approach. Linear superposition of photon-tunneling single-molecule models is used to create a multi-component spectral soft sensing model. Results show that this technique offers a new theoretical foundation for spectrophotometers to use in measuring multi-component solution concentrations, and that it solves the production problem of infinite formulas; simultaneously, it is discovered that the simultaneous determination technique for spectrophotometers based on the Lambert–Beer law is a special case of this technique.
The prevalence of distributed denial of service (DDoS) flooding assaults is one of the most serious risks to cloud computing security. These types of assaults have as their primary objective the exhaustion of the system’s available resources, that is, the target of the attack, in order to make the system in question unavailable to authorized users. Internet thieves often conduct flooding assaults of the kind known as DDoS, focusing primarily on the application and network levels. When the computer infrastructure is multi-mesh-geo distributed, includes multi-parallel services, and a high number of domains, it may be difficult to detect assaults. This is particularly true when a substantial number of domains are present. When there are a big number of independent administrative users using the services, the situation gets more complicated. The purpose of this body of research is to identify signs that may be utilized to detect DDoS flooding assaults; this is its main objective. As a result, throughout the course of our study, we established a composite metric that considers application, system, network, and infrastructure elements as possible indicators of the incidence of DDoS assaults. According to our research, DDoS assaults may be triggered by a combination of variables. Investigations of simulated traffic are being conducted in the cloud. High traffic may be the result of flooding assaults. The composite metric-based intrusion detection system will be the name of a one-of-a-kind intrusion detection system (IDS) that has been agreed upon ICMIDS. This system will use [Formula: see text]-Means clustering and the Genetic Algorithm (GA) to detect whether an effort has been made to flood the cloud environment. CMIDS employs a multi-threshold algorithmic strategy in order to identify malicious traffic occurring on a cloud-based network. Cisco has created this technology. This strategy necessitates a comprehensive investigation of all factors, which is crucial for assuring the continuation of cloud-based computing-based activities. This monitoring system involves the development, administration, and storage of a profile database, denoted as Profile DB. This database is used for recording and using the composite metric for each virtual machine. The results of a series of tests are compared to the ISCX benchmark dataset and statistical settings. The results indicate that ICMIDS has a reasonably high detection rate and the lowest false alarm rate in the majority of situations examined during the series of tests done to validate and verify its efficacy. This was shown by the fact that ICMIDS had the lowest false alarm rate among all examined conditions.
As people become more aware of environmental issues, Tesla, a pillar of the global electric vehicle market, has become a hot commodity in recent years. The prediction of Tesla’s share price is also a hot topic in the investment market. This experiment extracts sentiment factors of tweets about Tesla’s comments, combined with Tesla’s historical stock price as the dataset for training, testing and inspection. This experiment builds time series prediction models based on LSTM, XGBoost and RF algorithms to predict Tesla’s stock price, and evaluates the prediction effectiveness of the three algorithms based on the fit and error of the prediction results. The analysis of the data shows that XGBoost has the best fit and the lowest error among the three algorithms, and that the sentiment factor has its unique utility as raw data. The experimental results also empirically demonstrate the applicability of sentiment factor analysis and the three algorithms LSTM, XGBoost and RF in the field of stock price prediction.
Background noise, for example, can influence the outcome of target recognition in the visual communication design of weak target images, lowering the visual communication effect. In order to achieve this, this paper proposes a visual communication design method for weak target images based on spatiotemporal domain filtering. It uses guided filtering to smooth the image and raise the gray level of weak target points, and then obtains the background baseline of target points in the image sequence through the micro-partial equation to complete the image’s spatiotemporal domain background suppression. The best visual communication design result is achieved. The experimental findings demonstrate that the suggested approach has a good visual communication effect and that the pace of communication is not constrained by the size of the images.
Medical cancer rehabilitation healthcare center data maintenance is a global challenge with increased mortality risk. The Internet of Things (IoT)-based applications in healthcare were implemented through sensors and various connecting devices. The main problem of this procedure is the privacy of data, which is the biggest challenge with IoT, as all the connected devices transfer data in real time, the integration of multiple and other protocols can be hacked by the end-to-end connection, and it is not secure, security issues may crop up due to handling of such massive data in real time. Recent studies showed that a more structured risk assessment is needed to secure the medical cancer rehabilitation healthcare center data maintenance. In this respect, collaborative learning frameworks, such as Deep Federated Collaborative Learning (DFCL), are implemented for the study of medical cancer rehabilitation healthcare center data maintenance based on IoT-based systems and are proposed with smart short-term Bayesian convolution network systems for data analysis. This DFCL approach has been preferred in this context, strengthening privacy by allowing sensitive data to be retained. Experiments on benchmark datasets demonstrate that the federated model balances fairness, privacy, and accuracy. In this paper, we analyze administrative data count by medical stages taken from 2016 to 2022, the administrative data include data for routine operations. It is frequently used to assess by achieving an accuracy range of 19.8%. The leading diagnoses taken as per the patient's cost and stay count identifying a disease, illness, or problem by examining the unusual combination of symptoms made an accurate diagnosis which is 26% more efficient than the leading diagnosis. The hospital dictionary analysis is based on dictionary analysis count and data visualization summary; accuracy is 50% higher than the existing data visualization summary. By comparing the hospital dictionary, home health care analysis shows a 44.5% efficient analysis rate for patient data maintenance. Moreover, the adult day-care centers analyzed 88.6% efficient analysis rate for patient data maintenance with 750 patients.
Nowadays, Green IoT-Based Agriculture plays an essential role in farming to improve the yield. Here, IoT devices are embedded in the farming equipment, which helps to enhance the irrigation and yield with minimum cost-cutting. Data security and privacy are major challenges in green IoT-related agriculture. Therefore, a secured system should create to maintain data confidentiality, authentication, integrity, availability, and privacy. This system uses the privacy-preserving data aggregation (PPDA) with a Fair access framework (FAF) that manages the data security. The data aggregation concept is used to protect the green IoT data from false data injection. The FAF utilizes the blockchain technique to grant, get, revoke and delegate access to the user. The developed security system can adapt the green IoT-based agriculture and provide confidentiality, which is done with the help of an enhanced ciphertext access control mechanism. This system resolves the security and privacy issues involved in the Green IoT-based agriculture, and the effectiveness of the system is evaluated using implementation results.
Since the 1970s, China's economy has been changing rapidly, especially after joining the World Economic and Trade Organization, and with the increase in multinational enterprises, the development of overseas markets has become more convenient, contributing to the further strengthening of our economic power. The financial industry plays an essential role in the development of a country, and commercial banks are the leaders in the financial sector. They have made significant contributions to national economic growth. Under the background of substantial economic development, the economic management decisions of commercial banks have become more complex, which makes the risks faced by commercial banks increase continuously. The rapid growth of global informatization has effectively boosted the development of all walks of life. Based on the intelligent characteristics of big data, this paper analyzes the influencing factors of economic management decisions of commercial banks based on wise choices of big data, hoping to enable commercial banks to achieve the ideal expectation of financial management of "reducing risk and creating value" to enhance the competitiveness of commercial banks in the whole market.
Cloud computing has attracted significant attention because of the growing service demands of businesses that outsource computationally intensive tasks to the data center. Meanwhile, the infrastructure of a data center is comprised of hardware resources that consume a great deal of energy and release harmful levels of carbon dioxide. Cloud data centers demand massive amounts of electrical power as modern applications and organizations grow. To prevent resource waste and promote energy efficiency, virtual machines (VMs) must be dispersed over numerous physical machines (PMs) in a data center in the cloud. The actual allocation of VMs to PMs can involve more complex decision-making processes, such as considering the resource utilization, load balancing, performance requirements, and constraints of the system. Advanced techniques, like intelligent placement algorithms or dynamic resource allocation, may be employed to optimize resource utilization and achieve efficient VM distribution across multiple PMs. Cloud service suppliers aim to lower operational expenses by reducing energy consumption while offering clients competitive services. Minimizing large-scale data center power usage while maintaining the quality of service (QoS), especially for social media-based cloud computing systems, is crucial. Consolidating VMs has been highlighted as a promising method for improving resource efficiency and saving energy in data centers. This research provides deep learning augmented reinforcement learning (RL)-based energy efficient and QoS-aware virtual machine consolidation (VMC) approach to meet the difficulties. The proposed deep learning modified reinforcement learning-virtual machine consolidation (DLMRL-VMC) model can motivate both cloud providers and customers to distribute cloud infrastructure resources to achieve high CPU utilization and good energy efficiency as measured by power usage effectiveness (PUE) and data center infrastructure efficiency (DCiE). The suggested model, DLMRL-VMC, offers a VM placement approach based on resource usage and dynamic energy consumption to determine the best-matched host and VM selection strategy, Average Utilization Migration Time (AUMT). Based on AUMT, deep learning modified reinforcement learning (DLMRL) will choose a VM with a low average CPU utilization and a short migration time. The DLMRL-VMC Energy-efficient, Resource Allocation strategy is evaluated on the trace of the CloudSim VM to attain good PUE and CPU utilization.
With the rapid development of IoT, low-power wireless networks are becoming more and more important in engineering applications. For different IoT applications, a variety of corresponding low-power wireless technologies are studied and used. Among them, ZigBee technology is widely used for wireless data long-distance communication networks that require low-power consumption, low communication rate and large capacity self-organizing networks. However, the standard ZigBee module is limited by the large noise factor of the receiver and the small radiation power of the transmitter in the module, which cannot meet the requirements of long-distance communication. In this paper, we combine RF and communication technologies to design a long-range, low-power transceiver based on ZigBee technology for wireless data long-range communication simulation design and application. Experimental results show that the model in the urban area of 500[Formula: see text]m range, the system’s packet loss rate is kept at a low 0.03 or so, and the average Rssi is kept above and below [Formula: see text][Formula: see text]dBm, which can meet the general communication requirements and achieve the expected communication effect. Under the same packet rate, the model combines the improved AODVjr and Cluster_Tree algorithms, and the packet delivery rate can reach 98.5%, which greatly extends the wireless data long-distance communication life.
With the progress of information technology recently, mobile communication system edge computing (EC) has been widely used in all walks of life, but the traditional mobile communication system EC mode has security problems such as privacy disclosure, malicious tampering, and virus attacks. Computer algorithms has brought new vitality to EC in mobile communication systems. This paper analyzed the application of computer algorithm in EC mode of mobile communication system, and selected 20 users as the research object. This paper adopted traditional computing mode (such as cloud computing) and computer algorithm-based mobile communication system EC security research. This text compared the effects of two modes on security performance, data transmission efficiency, energy consumption, cost savings, and user satisfaction. The experimental results in this paper showed that the average security of EC mode of mobile communication system based on computer algorithm was 84%, and the average data transmission time was 4.8[Formula: see text]s. The energy consumption was 40%, and the cost savings and user satisfaction were 432,000 yuan and 13 points, respectively. Both were superior to the traditional edge counting mode. The EC mode of mobile communication system using computer algorithms can significantly improve the security of mobile communication, data transmission speed, cost savings, user satisfaction, and reduce energy consumption. This model has important significance and value for social development.
The purpose is to study the new application of artificial intelligence (AI) technology in the Agro logistics industry distribution modes. This work summarizes the problems in agricultural logistics by investigating the current situation of the logistics distribution modes (LDMs) of Agro products and establishes a joint LDM integrating logistics and agricultural industry chain (AIC). An intelligent virtual center is established for the joint LDM according to the specific situation of Xi’an. Experts are invited to evaluate the proposed LDM. The results show that the existing third-party logistics (3PL), agricultural supermarket docking LDM, and company+farmer shared LDM have respective advantages and disadvantages. The virtual center for the proposed joint LDM multiplies the weight matrix [Formula: see text] and fuzzy evaluation matrix A to obtain a comprehensive fuzzy evaluation result. It evaluates the result according to the maximum membership criterion. Most experts have a relatively good evaluation of the joint LDM. In the comprehensive fuzzy evaluation results, the comprehensive “excellent” score is 0.3755, and the comprehensive “good” score is 0.2678. According to the principle of maximum subordination, the AIC logistics integrating Agro products logistics has an excellent performance, and the overall satisfaction level of performance has exceeded 85%. In addition, Yonghui supermarket adopts the joint LDM, and the price of fruits and vegetables is lower than that of other LDMs. Therefore, the proposed AI-based joint LDM can optimize the distribution route, improve distribution efficiency, and save logistics costs to a great extent.
As with IoT devices, cloud computation increases its applicability to other areas that use the information and puts it in a commonplace that is necessary for computing and analysis. These devices need the cloud to store and retrieve data since they are unable to store and process data on their own. Cloud computing offers a variety of services to consumers, including IaaS, PaaS, and SaaS. The usage of cloud resources for data storage that may be accessible by all users associated with cloud computing is a key disadvantage of cloud computing. Without disclosing the data’s contents, the usage of Public Key Encryptions with Keyword Search (PEKS) secures publicly encrypted keys against third-party search capabilities that are not trustworthy. PEKs provide a security risk every time Interior Keywords Guessing Attacks (IKGA) are discovered in the system since an unauthorized service estimates the keywords in the trapdoor. This issue can be resolved using various methodologies such as the Certificateless Fleshed Public Key Authenticated Encryption of Keyword Search (CL-HPAEKS), which also uses Altered Elliptic Curve Cryptography (MECC), and the Mutation Centred Flower Pollinations Algorithm (CM-FPA), which uses optimization in keys to be improving the algorithm’s performance. The system’s security is achieved by adding a Message Fragments 5 (MD5) scramble mechanism. The proposed system achieves system security of 96%, and it takes less time to implement than earlier encryption methods.
