Intelligent Decision Research Articles

The impact of artificial intelligence on medical diagnostics: A letter to the editor

Madam, Artificial intelligence (AI) describes the generation of intelligent machines that are capable of carrying out activities that usually call for human intellect. (1) It involves creating algorithms and models that enable computers to learn from and analyze vast amounts of data, recognize patterns, make decisions, and even engage in natural language processing. (2) AI has become increasingly important in various fields due to its potential to automate complex processes, enhance efficiency, and drive innovation. It can transform industries, improve decision-making, and revolutionize how we live and work. In medical diagnostics, AI has had a profound impact on improving accuracy, efficiency, and patient outcomes. By leveraging machine learning algorithms, AI systems can process and analyze large volumes of medical data, including patient records, laboratory results, medical images, and genomic data. This allows for more accurate and timely diagnoses, as AI algorithms can detect subtle patterns and anomalies that may not be visible to human observers. AI-powered diagnostics can assist healthcare professionals in detecting diseases, predicting patient outcomes, and designing personalized treatment plans. (3) Furthermore, AI can contribute to the early detection of diseases, thereby enabling timely interventions and improving patient survival rates. For example, AI algorithms analyze medical imaging data, such as X-rays, MRIs, and CT scans, to detect abnormalities and potential signs of diseases like cancer. This helps radiologists and clinicians identify and diagnose conditions early when treatment options are more effective. (4) Additionally, AI aids in interpreting genetic data, allowing for the identification of genetic markers associated with specific diseases or drug responses. This information guides clinicians in selecting the most suitable treatment options for individual patients, leading to improved therapeutic outcomes. (5) AI in medical diagnostics also faces hurdles with data privacy and accessibility, as patient information must be handled securely. Ensuring AI algorithms are unbiased, and fair is a challenge to avoid disparities in healthcare outcomes. The interpretability of AI models is vital to building trust with healthcare professionals and facilitating their adoption. Regulatory compliance and ethical considerations are also significant factors that need to be addressed for the responsible and effective integration of AI in the medical field. In conclusion, Artificial Intelligence (AI) is a transformative technology with the potential to revolutionize various industries, including medical diagnostics. Its ability to analyze vast amounts of data, recognize patterns, and make intelligent decisions has proven invaluable in improving diagnostic processes accuracy, efficiency, and effectiveness. ---Continue

Deep Reinforcement Learning Lane-Changing Decision Algorithm for Intelligent Vehicles Combining LSTM Trajectory Prediction

Intelligent decisions for autonomous lane-changing in vehicles have consistently been a focal point of research in the industry. Traditional lane-changing algorithms, which rely on predefined rules, are ill-suited for the complexities and variabilities of real-world road conditions. In this study, we propose an algorithm that leverages the deep deterministic policy gradient (DDPG) reinforcement learning, integrated with a long short-term memory (LSTM) trajectory prediction model, termed as LSTM-DDPG. In the proposed LSTM-DDPG model, the LSTM state module transforms the observed values from the observation module into a state representation, which then serves as a direct input to the DDPG actor network. Meanwhile, the LSTM prediction module translates the historical trajectory coordinates of nearby vehicles into a word-embedding vector via a fully connected layer, thus providing predicted trajectory information for surrounding vehicles. This integrated LSTM approach considers the potential influence of nearby vehicles on the lane-changing decisions of the subject vehicle. Furthermore, our study emphasizes the safety, efficiency, and comfort of the lane-changing process. Accordingly, we designed a reward and penalty function for the LSTM-DDPG algorithm and determined the optimal network structure parameters. The algorithm was then tested on a simulation platform built with MATLAB/Simulink. Our findings indicate that the LSTM-DDPG model offers a more realistic representation of traffic scenarios involving vehicle interactions. When compared to the traditional DDPG algorithm, the LSTM-DDPG achieved a 7.4% increase in average single-step rewards after normalization, underscoring its superior performance in enhancing lane-changing safety and efficiency. This research provides new ideas for advanced lane-changing decisions in autonomous vehicles.

Study on the application of the intelligence teaching ecology based on the 5G blended campus network technology

Our society is being transformed by the technology emergence and the industrial revolution. The advances in the internet and artificial intelligence are reshaping the means of education, profoundly changing the ways of teaching and learning. This paper studies the pattern of how the new 5th generation blended campus network is applied to aid the new generation of intelligence teaching. This pattern is the implementation of national major policies and the measure of cultivating people. This paper introduces a new model for the intelligence teaching system. Based on the new model, distance interaction teaching system, VR practicing teaching system, intelligence testing system, and higher education intelligence decision system are developed. This model can be the basis of the informatization of future education.

Intelligent decision‐making for fertigation treatment of tomatoes cultivated in greenhouse: An experimental study

AbstractTo verify the effectiveness of the intelligent decision method for fertigation, an automatic control system for fertigation in greenhouses was designed, and three intelligent decision methods based on evapotranspiration (T1), soil moisture (T2) and accumulated temperature (T3) were tested. Intelligent decisions included monitoring meteorological information, automatically monitoring soil moisture, utilizing fertigation application systems and using automated control modules. The system was stable and accurately controlled according to the decision scheme. The results showed that the average errors of the automated control system for decision‐making and irrigation were 1.1 and 0.8%, respectively. The study findings serve as a reference for the integration of intelligent irrigation decision‐making and control systems and for further improving the efficiency of water and fertilizer utilized. Compared with those of the control, the three intelligent decision‐making methods increased the tomato yield by 8, 12 and 7%, respectively. In addition, the irrigation water and fertilizer levels decreased significantly compared with those in the control treatment. Although the accuracy of the soil water content (SWC) estimated based on ET and temperature in irrigation decision‐making is low, the general trend is consistent with practice. In addition, the irrigation water use efficiency (IWUE) and partial factor productivity of fertilizer (PFP) were significantly improved. Similarly, the IWUE in T1 was the highest (60 kg m⁻3), and the PFP in T3 was the highest (669 kg kg⁻¹).

Smart "Error"! Exploring Imperfect AI to Support Creative Ideation

Designers widely accept AI as a partner in the design process for its efficient and intelligent decision-making. However, AI is often not perfect, and AI error often makes humans dumbfounded. Literature has pointed out the value of such AI error, while still leaving its inspiration essence and application strategies uncharted from the practice perspective. This work focuses on bridging the practice gap by looking into and exploiting the imaginative "mislabeled" objects of object detection models. To gain insights into the inspiration of AI "error", we collected a dedicated AI "error" dataset from object detection and invited eight designers to share divergent comments on the "mislabeled" objects. Coding was then performed on the comments, which summarizes the inspiration of AI "error" into six atomic dimensions. Subsequently, we took a step further to an exploratory study, a comparative ideation experiment with 20 designers, investigating how to apply these inspiration dimensions to create ideas. Questionnaire and interview results revealed that essential inspiration of AI "error" could positively activate creativity, especially the "Outline" dimension. A design model CETR is then formulated by summarizing the application of atomic inspiration of "error" into four forms of creativity, which could be taken as a guideline for cooperative design with AI "error". In addition, we also sketch two approaches to generate more inspiring and applicable AI "error", elaborate on two principal characteristics of AI "error" for promoting creativity, and propose three strategies for better co-creating with AI "error". Finally, we provide insight into design research about AI self-awareness and human-AI collaboration.

The combination of Multi-factor Models and Artificial Intelligence / Machines

With a focus on the rationality, risk, and return components of investment strategies, this study proposes to provide light on the growing importance of combining multifactor models and artificial intelligence (AI) in financial decisions intelligent decision making. The research aims to bring together traditional finance theories and modern data-driven approaches to enhance investment decision-making, risk management, and portfolio optimization. In this research I will mainly use this experimental scheme of data exploration as well as other experimental scheme such as using model training and testing to evaluate the possibility of AI-driven models to provide practical solutions and valuable knowledge for the financial industry.

Energy efficiency security in urban areas: Challenges and implementation

In this paper, we explore the transformative potential of data-driven methodologies in the intricate landscape of urban energy transactions, with a keen focus on enhancing decision-making intelligence, amplifying the integration of renewable energy sources, and fostering active community engagement. A pivotal contribution to this research lies in the introduction of an innovative Intrusion Detection System (IDS) fortified by the robust capabilities of deep evolving convolutional Generative Adversarial Networks (GANs). This novel security measure is designed to fortify urban energy transactions against potential cyber threats, ensuring the integrity and reliability of the energy infrastructure. Furthermore, our study introduces a groundbreaking hybridization of the Bat Algorithm (BA) and Teaching-Learning-Based Optimization (TLBO) for the optimization of GAN model training. This fusion of optimization techniques enhances the efficiency and accuracy of the training process, elevating the reliability of the generated models within the context of urban energy management. To validate and demonstrate the practical applicability of these innovations, we deploy them on the real-time dataset of a smart city's digital twin. This implementation serves as a tangible proof of concept, offering valuable insights into the performance and adaptability of the proposed approaches within real-world urban energy scenarios. As the findings unfold, it becomes evident that our research significantly contributes to the evolution of urban energy management, providing a robust foundation for the development of secure, resilient, and community-centric smart city energy systems. Through these advancements, we pave the way for a sustainable urban future where energy transactions are not only efficient but also secure and inclusive, aligning with the broader goals of smart city development.

Automatic Self Driving Car

This project deals with building an autonomous car that can travel safely and intelligently avoiding the risk of human errors. This raspberry Pi based project can detect the obstacles & traffic light. It can compare the data processed with the data provided to it and is able to take an intelligent decision whether to stop or continue its present path. Important components involved in this project are - the hardware platform which includes raspberry pi board, all the hardware like pi camera and the ultrasonic sensor for improved efficiency & the camera used along with an ultrasonic sensor to provide necessary data from the world for real time processing and application. Second being the cloud platform which will be basically used to train our raspberry pi board for real time applications. Cloud helps us to test as well as train better tracking and decision models & helps in providing the offline computing and storage capabilities for vehicle. Basically, it will be used to train the processor to differentiate between positive (green signal) and negative (red signal) images using various thousands of such signal images as an example. The third and most important part includes the algorithms for perception, control, localization, and recognition. Key Words: Raspberry pi, L293D Driver, Machine Learning, Open CV, Ultrasonic Sensor & Pi Camera.

A real-time feeding decision method based on density estimation of farmed fish

With the global population growth and increasing demand for high-quality protein, aquaculture has experienced rapid development. Fish culture management and feed supply are crucial components of aquaculture. Traditional baiting management relies on experiential judgment and regular observation, which often leads to inefficient baiting practices and wastage. To address these issues, intelligent bait casting decisions have emerged. Leveraging advanced artificial intelligence algorithms, intelligent bait casting decisions can overcome most drawbacks of traditional bait management and enhance breeding efficiency. However, most of the current intelligent baiting decisions are focused on using methods such as image processing and target detection to identify different feeding actions and patterns. These methods do not discuss based on video streams and do not consider the changes in fish behavior during the baiting process. Therefore, we proposed a real-time analysis method based on the density distribution of fish feeding behavior (FishFeed). Firstly, this method upgrades the input mechanism, not only handling static images but also capable of real-time video stream analysis. Secondly, by evaluating the fish school density distribution through a new intelligent baiting strategy, this method can monitor the feeding behavior of fish school during the baiting process in real time. Finally, we constructed a dataset for fish school density analysis (DlouFishDensity) that includes a wealth of video and image frames, providing a valuable resource for research. Experimental results indicate that our algorithm outperforms MCNN, improving MAE by 1.63 and 1.35, MSE by 1.92 and 1.58, and reducing prediction time by 2.56 seconds on the same dataset. By implementing real-time analysis of fish feeding behavior density distribution, our method offers a more efficient and effective approach to baiting management in aquaculture, contributing to improved breeding efficiency and resource utilization.

Automated Classification of Abdominal Ultrasound lmages of the Pancreas Based on the Spectral Representation of the Border’s Contours

The purpose of the research is to develop a methodology for classifying complexly structured halftone images based on a multimodal approach using methods of morphological analysis, spectral analysis and neural network modeling.Methods. А method for classifying the contours of the boundaries of segments of a complexly structured image is described. Тhe method is based on the fact that in chronic diseases of the pancreas, there is a violation of the integrity of the contour of its border and its waviness increases due to retractions and bulges caused by an alterative inflammatory process. Тhe method includes the stages of normalization of ultrasound images and image segmentation with the selection of the contour of the object of interest. Тo classify the contour of a segment boundary, it is proposed to use Fourier analysis and neural network technologies. Тhe method is illustrated using the example of classifying the contour of the border of the pancreas on its transcutaneous acoustic image.Results. Еxperimental studies of the proposed methods and means for classifying medical risk were carried out on diagnostic tasks according to the following classes: "chronic pancreatitis" – "without pathology". For experimental studies, video sequences of ultrasound images of the pancreas provided by an endoscopist were used. Тhe purpose of the experimental studies was to analyze the classification quality indicators of image classifiers with class segments "Chronic pancreatitis" and "Without pathology". Тhe training sample of video images (frames of video sequences) included 200 examples, one hundred from each class. Тhe quality indicator "Sensitivity" of classification for two classes is 85,7%, the indicator "Specificity" is 87,1%.Тhe use of the contour analysis method in classifiers of ultrasound images of the pancreas opens up new opportunities for accessible and objective diagnosis of pancreatic diseases, expanding the capabilities of intelligent clinical decision support systems.

Ai-Powered Customer Experience: Personalization, Engagement, and Intelligent Decision-Making in Crm

There has been a recent increase in interest regarding the remarkable potential of artificial intelligence (AI) to profoundly transform online advertising. The purpose of this research is to critically assess how AI can enhance customer experience (CX) in various business applications. We aim to identify important concepts, evaluate the impact of AI-powered CX initiatives, and offer suggestions for future research. By conducting a thorough analysis of academic publications, industry reports, and case studies, this study extracts theoretical frameworks, empirical findings, and practical insights. The results highlight the significant changes that occur with the integration of AI into Customer Relationship Management (CRM). AI enables personalized interactions, strengthens customer engagement through interactive agents, provides data-driven insights, and empowers informed decision-making throughout the customer journey. Four key themes emerge from research findings: personalized service, improved engagement, data-driven strategy, and intelligent decision-making. However, challenges such as data privacy concerns, ethical considerations, and potential negative experiences with poorly implemented AI persist. This article makes a valuable contribution to the AI in CRM discourse by summarizing the current state, exploring key themes, and suggesting future research opportunities. It is strongly advocated for responsible AI implementation, emphasizing ethical considerations and providing guidance to organizations as they navigate the opportunities and challenges presented by AI.

Proposal and Definition of an Intelligent Clinical Decision Support System Applied to the Prediction of Dyspnea after 12 Months of an Acute Episode of COVID-19.

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice.

Analysis of Influencing Factors of Economic Management Decision of Commercial Banks Based on Big Data Intelligent Decision

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.

Context-Aware IoT System Development Approach Based on Meta-Modeling and Reinforcement Learning

Integrating context awareness into the Internet of Things systems is essential for enhancing their adaptability to their context, particularly their user preferences and behaviors. This paper proposes an approach to model and develop context-aware self-adaptive IoT systems, capable of adapting their actions according to their users’ preferences. The approach consists of three main axes. The first axis involves establishing an overview of the system architecture that provides a high-level understanding of the various components of a context-aware IoT system. The second axis concerns the creation of a context-aware IoT systems meta-model, encapsulating the essential elements, relationships, and dependencies governing context awareness within the IoT system in a domain-independent manner. The third axis proposes a reinforcement learning reasoning process to enable intelligent decision-making within context- aware IoT systems. To validate the feasibility of the proposed approach, a simulation was conducted using the OpenAI Gym framework to emulate a context-aware smart home system. The results highlight the feasibility of the approach, and its potential to enhance real-life IoT systems’ awareness of their users’ context.

Multi-head attention-based intelligent vehicle lane change decision and trajectory prediction model in highways

With the aim to improve the interaction between intelligent vehicles and human drivers, this article proposes the MCLG (multi-head attention + convolutional social pooling + long short-term memory + Gaussian mixture model) lane change decision and trajectory prediction model, which includes a lane-changing intention decision module. The model comprises a lane change decision module responsible for determining three lane change intentions: left lane change, right lane change, and car-following. Subsequently, a multi-head attention mechanism processes complex vehicle interaction information to enhance modeling accuracy and intelligence. In addition, uncertainty in trajectory prediction is considered by using multimodal trajectory prediction and Gaussian mixture model, and diversity and uncertainty are combined by combining trajectory prediction from several different modalities through probabilistic combinatorial sampling patterns. Test results indicate that the MCLG model, based on the multi-head attention module, outperforms existing methods in trajectory prediction. The decision module, which takes interactive information into account, exhibits higher predictability and accuracy. Furthermore, the MCLG model, considering the lane-changing decision module, significantly enhances trajectory prediction accuracy, providing robust decision-making support for autonomous driving systems.

Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review.

Reinforcement learning (RL) has emerged as a dynamic and transformative paradigm in artificial intelligence, offering the promise of intelligent decision-making in complex and dynamic environments. This unique feature enables RL to address sequential decision-making problems with simultaneous sampling, evaluation, and feedback. As a result, RL techniques have become suitable candidates for developing powerful solutions in various domains. In this study, we present a comprehensive and systematic review of RL algorithms and applications. This review commences with an exploration of the foundations of RL and proceeds to examine each algorithm in detail, concluding with a comparative analysis of RL algorithms based on several criteria. This review then extends to two key applications of RL: robotics and healthcare. In robotics manipulation, RL enhances precision and adaptability in tasks such as object grasping and autonomous learning. In healthcare, this review turns its focus to the realm of cell growth problems, clarifying how RL has provided a data-driven approach for optimizing the growth of cell cultures and the development of therapeutic solutions. This review offers a comprehensive overview, shedding light on the evolving landscape of RL and its potential in two diverse yet interconnected fields.

Evaluation of resampling techniques for deep learning based identification of promising genotypes in sugarcane varietal trials

Deep learning is a class of machine learning algorithms that extract high-level features from the raw input for making intelligent decisions. Identification of promising genotypes in varietal trials is one of many agriculture domain applications requiring implementation of deep learning to perform intelligent decision using varietal trial data. However, it has been found that varietal trial data to be used for identification is highly imbalanced one providing great challenges for classification tasks in deep learning. For example, only 33 genotypes were identified as promising in zonal varietal trials of All India Coordinated Research Project (AICRP) on Sugarcane during 2016-21, while those of non-promising class are 148. Balancing an imbalanced class is crucial as the classification model, which is trained using the imbalanced class dataset will tend to exhibit the prediction accuracy according to the highest class of the dataset. One way to address this issue is to use resampling, which adjusts the ratio between the different classes, making the data more balanced. Study was conducted to implement and evaluate four resampling techniques viz. random undersampling, random oversampling, ensemble, SMOTE to balance varietal trial dataset in order to build deep learning model to identify promising genotypes in sugarcane. Paper describes the methodology used in our approach for building deep learning model using resampling techniques and then presented comparative performance of these approaches in identifying promising genotypes. Results indicate that SMOTE and random oversampling performed well for balancing imbalanced dataset for developing deep learning model in comparison to no-resampling of imbalanced dataset. SMOTE outperformed all resampling techniques by achieving high values of precision, recall and F1 score for both positive and negative classes. However, ensemble and random undersampling methods did not showed good results in comparison to SMOTE and random oversampling technique. Studies conducted will be useful in developing artificial intelligence based tools for automatic identification of promising genotypes in varietals trials of sugarcane in particular, as well as other crops in general.

Enterprise chatbots in managers' perception: a strategic framework to implement successful chatbot applications for business decisions

PurposeThe contemporary business environment is characterised by an increasing reliance on artificial intelligence, automation, optimisation, efficient communication and data-driven decision making. Based on the limited academic literature that examines the managerial perspective on enterprise chatbots, the paper aims to explore organisational needs and expectations for enterprise chatbots from a managerial perspective, assesses the relationship between managerial knowledge and managerial opinion regarding enterprise chatbots, and delivers a framework for integrating chatbots into the digital workforce.Design/methodology/approachThe paper presents a quantitative design. An online, self-administered survey yielded 111 valid responses from managers in service and manufacturing organisations based on convenience and snowball sampling strategies. Given the nature of the data and the research questions, the research was conducted using principal component analysis, parallel analysis, correlation, internal consistency and difference in means tests.FindingsThis research explores the managerial perspective on enterprise chatbots from multiple perspectives (i.e., adoption, suitability, development requirements, benefits, barriers, performance and implications), presents a heat map of the average level of chatbot need across industries and business units, highlights the urgent need for education and training initiatives targeted at decision makers, and provides a strategic framework for successful chatbot implementation.Practical implicationsThis study equips managers and practitioners dealing with enterprise chatbots with knowledge to effectively leverage the expected benefits of investing in this technology for their organisations. It offers direction for developers in designing chatbots that align with organisational expectations, capabilities and skills.Originality/valueInsights for managers, researchers and chatbot developers are provided. The work complements the few academic studies that examine enterprise chatbots from a managerial perspective and enriches related commercial studies with more rigourous statistical analysis. The paper contributes to the ongoing discourse on decision-making in the context of technology development, integration and education.

Research on Intelligent Decision-making Problems Based on Intelligent Computing

Research on Intelligent Decision-making Problems Based on Intelligent Computing

Research on Manned and Unmanned Collaborative Mission Planning Based on Intelligent Decision-making of the ABC (Artificial Bee Colony) Algorithm

Research on Manned and Unmanned Collaborative Mission Planning Based on Intelligent Decision-making of the ABC (Artificial Bee Colony) Algorithm