Complex Decision Support and Intelligent Optimization Systems Based on Artificial Intelligence Large Models

With the surge in data volume and dimensions, existing decision-making systems are difficult to capture nonlinear relationships and cope with the complexity and diversity of data, which leads to decision lag. To solve this problem, this article studies an intelligent decision-making system. The system integrates multi-layer feedforward neural networks and decision tree algorithms, and designs a distributed computing architecture based on the Apache Spark platform. Through the integration of the big data platform, the system integrates and processes multiple data sources; then, a three-layer feedforward neural network model is adopted, and a specific mechanism is used to prevent overfitting. After that, the decision tree algorithm is used to deeply explore the complex relationships in the data; Apache Spark is applied to accelerate the calculation process and efficiently query and analyze the stored data. The system also supports online learning strategies to continuously update the model to adapt to new data changes. The research results show that in the field of financial risk analysis, the average accuracy of the intelligent decision-making system in this article is 92.29 %, and the average response time is only 0.013 seconds; the average accuracy and response time of the traditional system are 82.54 % and 0.034 seconds respectively; the system in this article is significantly better than the traditional system. According to the study, an intelligent decision-making system not only increases decision-making's efficiency and accuracy but also offers solid base for more intelligent decisions in the future.

In the manufacturing industry, priority is given to quality models in the product, in order to make companies competitive, that is why it has been proposed to implement haptic interfaces capable of detecting anomalies in the operation of the electronic equipment of the companies. Cars in the manufacturing sector, that is why through the implementation of Artificial Intelligence and Smart Manufacturing Models, we can get to build intelligent systems for the correct decision making in the auto parts sector in Mexico. Intelligent manufacturing is a subset that employs various techniques of artificial intelligence and emerging technologies coupled with computer control and high levels of adaptability to adapt to changes in product improvement. Intelligent manufacturing is focused on taking advantage of advanced information technologies and even intelligent analysis of data and manufacturing via the Internet of things to allow flexibility in physical processes to address a dynamic market in each society and from a global perspective. There is more training related to the implementation of artificial intelligence of the workforce for such flexibility of adaptability of products and use of emerging technology instead of specific tasks as is usual in traditional manufacturing, and which requires a larger group of individuals for it.

A multitenant SaaS is a single codebase and run-time customizable application supporting multiple tenants and users. There is a huge demand for multitenant applications as it reduces total cost of ownership for both, service provider as well as consumers. However, Multitenant SaaS applications face fluctuating load problem due to variable tenants and users. Thus, managing resources becomes difficult. Traditionally, to overcome this issue, resources are managed and scaled using monolithic architecture which may lead to underutilization and over utilization of resources in cases of low load and high load respectively. Microservices architecture is a new exemplar for developing cloud SaaS applications as it has several advantages over monolithic. The paper aims to analyze the performance and scalability analysis of multitenant SaaS application using both microservices and monolithic architecture. Utility Booking application is developed for a case study. Scalability analysis is performed, and the performance of application using both approaches is compared using average response time and throughput. Results show that multitenant applications using microservices with large number of concurrent users outperforms monolithic approach and are resource efficient as microservice application scales dynamically based on varying load.

In the present article an effort has been made to design and develop a diet recommendation system for Metabolic Disorders patients. The key feature of this system is to recommend a menu for dinner to maintain nutritional micros as per daily requirements.The proposed intelligent decision-making system is designed as per the following phases:Under the 1st Phase, we compute the requirement of calories as per the Patient's personal information (like sex, age, height, weight), physical activity, environmental situations, and food habits on a daily basis. Under the 2nd Phase, development of knowledge base as per Patient's foods habits information. 3rd Phase is based on designing the recommendation system for a dinner menu to maintain nutritional micros as per daily requirements. The results of the system have been validated by using the Degree of match algorithm and comments of Nutritional experts. This intelligent decision making system will help the ordinary people living in urban and rural areas, especially those not aware of the nutritional value concerned with their daily food items.

This study focuses on energy consumption optimization and green energy-saving regulation of intelligent buildings based on Deep Reinforcement Learning (DRL). The construction sector accounts for an important proportion of global energy consumption and carbon emissions, and traditional energy-saving methods are difficult to meet the needs of complex operating environments due to lack of adaptability. DRL can effectively deal with high-dimensional data and complex decision-making problems by combining the feature extraction ability of deep learning and the decision-making optimization ability of reinforcement learning. In this study, a DRL-driven intelligent building energy consumption optimization framework is constructed, multi-source data fusion is realized by integrating internal and external data of the building, and a real-time control algorithm is designed to realize intelligent collaborative optimization among devices. The experiment adopts the simulation environment encapsulated by the interface of EnergyPlus 9.5 and OpenAI Gym, takes the three-story ASHRAE medium-sized office as a model, and carries out the simulation based on the data of typical meteorological years in Beijing. The results show that the energy consumption of DRL control strategy in typical summer weeks is 6,940kWh, the energy saving rate reaches 22.6%, and the PMV compliance rate is 93%, which is significantly better than PID control and rule scheduling strategy based on ASHRAE 90.1 standard. Through precooling strategy and dynamic inertia compensation, DRL control effectively reduces air conditioning load and transient response energy consumption, and performs well in temperature fluctuation range and stability of control variables. This study provides a theoretical basis and technical scheme for energy saving of intelligent buildings, and shows the great potential of DRL in the regulation of green energy saving.

Managing Attribute Complexity for User-centered Decision Support Systems

: This report describes the work performed in the second year of the three-year contract to explore the application of symbolic processing to command and control (C2); specifically, the graphics interface between the C2 user and a complex C2 decision support system. Volume III contains the programs that manipulate the knowledge base and provide the active behavioral component of the system.

Microservice architecture has started a new trend for application development/deployment in cloud due to its flexibility, scalability, manageability and performance. Various microservice platforms have emerged to facilitate the whole software engineering cycle for cloud applications from design, development, test, deployment to maintenance. In this paper, we propose a performance analytical model and validate it by experiments to study the provisioning performance of microservice platforms. We design and develop a microservice platform on Amazon EC2 cloud using Docker technology family to identify important elements contributing to the performance of microservice platforms. We leverage the results and insights from experiments to build a tractable analytical performance model that can be used to perform what-if analysis and capacity planning in a systematic manner for large scale microservices with minimum amount of time and cost.

The logistics industry currently faces a prevalent "data silo" issue. This leads to information isolation and system fragmentation and severely restricts the ability of cross-platform business collaboration and intelligent decision-making. To address this problem, this paper proposes a non-intrusive logistics system architecture design for data silo integration. The architecture is based on key mechanisms such as edge access, protocol abstraction, and a unified data bus. It realizes lightweight integration of heterogeneous logistics information systems, and features low coupling, high compatibility, and strong adaptability. The designed system includes acquisition layer protocol adaptation components, transport layer edge routing mechanisms, and platform layer intelligent processing engines. It supports multi-protocol concurrent access and fault self-recovery capabilities. Deployment and testing in enterprise scenarios show that the system outperforms traditional solutions in data acquisition delay control, stability maintenance, and multi-protocol adaptation. The non-intrusive system not only leads significantly in throughput, reaching 12,000 entries per second. This is much higher than the 7,500 entries per second of traditional Application Programming Interface (API) integration and the 9,200 entries per second of the Enterprise Service Bus (ESB) architecture. It also performs excellently in average response time, which is only 45 milliseconds, significantly lower than the other two solutions. In particular, this system achieves efficient integration without modifying the original systems and effectively solves the problem of coexisting multi-systems and non-interoperable data in enterprises. This paper provides a promotable reference architecture for the informatization transformation of logistics.

Currently, manufacturing industries focus on intelligent manufacturing. Prediction and monitoring of tool wear are essential in any material removal process, and implementation of a tool condition monitoring system (TCMS) is necessary. This work presents the flank wear prediction during the hard turning of EN8 steel using the deep learning (DL) algorithm. The turning operation is conducted with three levels of selected parameters. CNMG 120408 grade, TiN‐coated cemented carbide tool is used for turning. Cutting force and flank wear are assessed under dry‐cutting conditions. DL algorithms such as adaptive neuro‐fuzzy inference system (ANFIS) and convolutional auto encoder (CAE) are used to predict the flank wear of the single‐point cutting tool. The DL model is developed with turning parameters and cutting force to predict flank wear. The different ANFIS and CAE models are employed to develop the prediction model. Grid‐based ANFIS structure with Gauss membership function performed better than ANFIS models. The ANFIS model’s average testing error of 0.0074011 mm and prediction accuracy of 99.81% are achieved.

As flexibility and agility become key success factors of competitive oil business industries, the ability to support the short-term decision and prediction making of oil exploration, scheduling, oil availability and yield prediction becomes a critical issue. This research paper presents a rule-based expert knowledge system, i.e. web-based hybrid expert system, called intelligent hybrid decision and prediction system (IHDPS) run on the cyber-enabled .NET Expert System Shell (NESS) technology platform to addresses how engineering knowledge can be dynamically represented and efficiently utilised in oil business industries. The rule-based knowledge system, called web-based IHDPS, is designed and implemented using the rule-based inference, reasoning and decision-making approach. The distinctive technical contributions of IHDPS focus on three critically integrated elements: (1) a spreadsheet software for interpreting and evaluating performance data, (2) a knowledge rule for time-series pattern recognition, and (3) an embedded application component.

Hospital Bed Capacity (HBC) planning affects economic and social sustainability in healthcare through bed capacity efficiency and medical treatment accessibility. Conventionally, this problem is solved using programming or simulation models with assumptions and limits. Forecasting the HBC using time series data on bed occupancy has been considered but not with factors such as the Number of Hospitalized Patients (NHP) and patient’s length of stay (LOS). This study proposes a data-driven methodology to forecast the HBC using Machine Learning (ML) and Deep Learning (DL). The LOS classification is performed using several ML techniques, including the Bayesian network, K-nearest neighbor, support vector machine, decision tree, and Linear regression. Also, the seasonal autoregressive integrated moving average, linear regression and Long short-term memory neural network are applied for the NHP forecasting. The forecasting and descriptive analysis outputs based on LOS classes are directly applied to a simple mathematical model to predict the required bed capacity. This methodology is applied in a case study in a heart ward at a public hospital. The data set includes 51231 records, and ML and DL algorithms are developed in Python. Results show that the heart ward’s bed capacity must be raised from 45 to 137 by 2026. In addition, several managerial recommendations are formulated.

This paper presents how the evidential reasoning approach for multi-criteria decision analysis, with the support of its software implementation, Intelligent Decision System (IDS), can be used to analyse whether low level radioactive waste should be stored at the surface or buried deep underground in the territory of the community of Mol in Belgium. Following an outline of the problem and the assessment criteria, the process of using IDS for data collection, information aggregation and outcome presentation is described. The outcomes of the analysis are examined using the various sensitivity analysis functions of IDS. The analysis using IDS can generate informative outcomes to better support the decision making process in practice.

Decision making tasks are subject to limitation and also depends on human knowledge, experiences, judgments and preferences. In this case, Intelligent Decision System (IDS) technologies can be used to provide realistic and consistent decisions, besides to improve the effectiveness of decision making processes. Intelligent Decision Support System (IDSS) is a contributory of IDS technology to assist decision makers in high level phases of decision making by integrating human knowledge with modeling tools. Nowadays, data mining (DM) techniques is also can be used to support Knowledge Management (KM) tasks especially for knowledge discovery and knowledge engineering. DM is emerging data analysis tool and widely used in order to produce valuable knowledge for decision making as knowledge modeling task. In Human Resource (HR), managing talent is among the challenges of HR professionals which can be handle by using IDSS and data mining technologies. For that reason, in this article, we discussed the potential to uses IDSS approach for talent management using DM techniques by proposing IDSS architecture and a case study on talent classification. This study consists of three parts; the first part is to know the previous works on IDSS, talent management and DM. The second part is discussion on proposed IDSS architecture for talent management. Finally, the third part is a case study on the use of DM classification method for talent forecasting, especially for employee's job promotion.

The use of driver behavior has been considered a complex way to solve road safety complications. Car drivers are usually involved in various risky driving factors which lead to accidents where people are fatally or seriously injured. The present study aims to dissect and rank the significant driver behavior factors related to road safety by applying an integrated multi-criteria decision-making (MCDM) model, which is structured as a hierarchy with at least one 5 × 5 (or bigger) pairwise comparison matrix (PCM). A real-world, complex decision-making problem was selected to evaluate the possible application of the proposed model (driver behavior preferences related to road safety problems). The application of the analytic hierarchy process (AHP) alone, by precluding layman participants, might cause a loss of reliable information in the case of the decision-making systems with big PCMs. Evading this tricky issue, we used the Best Worst Method (BWM) to make the layman’s evaluator task easier and timesaving. Therefore, the AHP-BWM model was found to be a suitable integration to evaluate risky driver behavior factors within a designed three-level hierarchical structure. The model results found the most significant driver behavior factors that influence road safety for each level, based on evaluator responses on the driver behavior questionnaire (DBQ). Moreover, the output vector of weights in the integrated model is more consistent, with results for 5 × 5 PCMs or bigger. The proposed AHP-BWM model can be used for PCMs with scientific data organized by traditional means.