Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences

Volume Info.

  • Title

    Proceedings of the 2023 International Conference on Machine Learning and Automation

    Conference Date






    978-1-83558-311-1 (Print)

    978-1-83558-312-8 (Online)

    Published Date



    Mustafa İSTANBULLU, Cukurova University


  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230797

    Application of artificial intelligence and machine learning in financial forecasting and trading

    Artificial Intelligence (AI) and Machine Learning (ML) have been employed in the field of financial forecasting for many years. Existing scientific research has substantiated the effectiveness of AI/ML in the financial market. Currently, approximately 35% of the total capitalization of the US stock market is influenced by quantitative analysis, primarily consisting of AI/ML methodologies and their variants. This paper reviews classical advanced methodologies of AI/ML in financial forecasting and automated trading. Specifically, it focuses on discussing representative methods from three categories: statistical approach (including ARIMA-GARCH), machine learning approach (including SVM and LSTM), and the logistic approach (including the Fuzzy System). In detail, this paper delves into the fundamental aspects of each method and illustrates their effectiveness through existing results from relevant papers. The structure of this paper begins with the introduction of each method, followed by their applications, and a discussion of their pros and cons. Furthermore, this paper offers an outlook on the hotspots and prospects for the development of this research topic.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230799

    Investigation and designing a comprehensive supply chain database for Walmart

    In light of the rapid progression of digital technology, fierce competition, and the ever-shifting demands of consumers, the contemporary retail sector is continually evolving. This underscores the critical need to embrace digital solutions. This study seeks to aid Walmart, one of the world's largest retailers, in improving supply chain efficiency, optimizing inventory management, and enhancing the overall customer experience by creating a comprehensive supply chain database. Specifically, we employ the core entity method to identify Walmart's key entity, which is the "product," and ascertain its associated entities. Subsequently, we establish relational models based on the attributes and relationships of each table, considering real-world scenarios, such as one-to-many and many-to-many relationships. We employ normalization techniques to ensure the proper utilization of functional dependencies and candidate keys for each entity. This guarantees that the designed tables adhere to the principles of the First, Second, and Third Normal Forms, preserving data integrity while minimizing redundancy. Furthermore, we create a set of logical database commands, illustrated through flowcharts, and rigorously test their functionality and efficiency. Lastly, we explore potential directions for future research. The successful implementation of this design relies on robust data management practices and a reliable database system tailored to meet Walmart's specific needs. This results in a logically structured and standardized supply chain database. The research findings underscore the vital importance of a well-conceived supply chain database in addressing the challenges faced by large-scale retail operations like Walmart supermarkets.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230800

    Investigation related to database design for letter of credit

    The "Letter of Credit" is a fundamental payment mechanism in global trade, and its efficiency has significantly declined due to the impact of the COVID-19 pandemic. This document delves deeply into the development and execution of a comprehensive database for managing Letters of Credit (L/Cs). It is designed with a real-world case study derived from the International Settlement Department of the Bank of China. The central goal of this database is to enhance the efficient management and simplification of the complex procedures inherent in L/C transactions. The development process follows a systematic approach, commencing with the establishment of three fundamental assumptions to delineate the scope of the study. Subsequently, the database design progresses through stages involving Entity-Relationship Diagram (ERD) development, relational model construction, and normalization procedures. These steps collectively culminate in the creation of an intelligently structured database comprising thirteen distinct tables. In conclusion, this paper not only delves into the design and implementation of the L/C database but also showcases its practicality through real-world scenarios. The database, fueled by meticulous design and thoughtful implementation, is poised to revolutionize the management of L/C operations, offering enhanced efficiency and accuracy in international trade transactions.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230804

    Traffic light control with reinforcement learning

    Urban traffic signal optimization is important for alleviating congestion in urban transportation systems. This study proposes a real-time traffic light control algorithm based on deep Q learning with a reward function that accounts for queue lengths, delays, travel times, and throughput. The model dynamically decides phase changes based on current traffic conditions. The training of the deep Q network involves an offline stage from pre-generated data with fixed signal timing and an online stage using real-time traffic data. A deep Q network structure with a“phase gate component is used to simplify the model's learning task under different phases. A“memory palace" mechanism is used to address sample imbalance during the training process. Both synthetic and real-world traffic flow data are used to validate our approach under an urban road intersection scenario in Hangzhou, China. Results demonstrate significant performance improvements of the proposed method in reducing vehicle waiting time (57.1% to 100%), queue lengths (40.9% to 100%), and total travel time (16.8% to 68.0%) compared to traditional fixed signal timing plans.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230805

    Research on the application of deep learning algorithms to PCB defect detection

    In today's electronics industry, Printed Circuit Boards play a crucial role in providing the layout for circuit components and conductive traces in nearly all electronic devices. The quality of components soldered onto Printed Circuit Boards directly impacts product performance. To ensure the performance of electronic devices, Printed Circuit Boards defect detection based on deep learning algorithms has become a pivotal technology in the defect inspection process within the electronics industry. However, the application of deep learning algorithms in this context faces several challenges. These challenges include difficulties in acquiring Printed Circuit Boards defect datasets, limited generalization capability in Printed Circuit Boards defect detection, and slow and low-quality Printed Circuit Boards image stitching processes. To enhance researchers' understanding of deep learning-based Printed Circuit Boards defect detection, this paper analyzes the challenges associated with deep learning in the Printed Circuit Boards defect detection process and proposes several viable solutions. In conclusion, this paper provides insights into the future of deep learning-based Printed Circuit Boards defect detection.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230807

    Conversational agent in HCI a review

    Nowadays, AI technology is developing rapidly and slowly covering all areas of life. AI facilitates different parts of our lives and provides us with a lot of help. For example, in learning, students can acquire knowledge more conveniently through AI, and buyers can find suitable goods more conveniently through AI when shopping. At the same time, more and more technology is used in the field of voice agents, which allows humans to enjoy a lot of better services. In this article, we will study to better understand "how to understand human natural language and how the repository of knowledge is built." In the article we build with examples and deep learning models (CNN and RNN) through databases. Through repeated research and analysis, we can find that there are some limitations in this paper, such as the single learning model and the insufficient elaboration of data analysis and signal system technology. But at the same time, we also found a lot of future application prospects that voice agents can develop, it can be applied in many fields, such as finance, medical care, education and so on. For example, in children's education, parents can use voice agents to set time limits and monitor their children's progress. Existing digital interactive storytelling systems have limitations in terms of available storybooks and hand-crafted issues. Voice agents are becoming more popular in everyday scenarios, and more users are adopting devices like Siri and Google Assistant. In the future, conversational agents are expected to play an important role in oral communication with users.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230808

    Application of AI conversation agent in new frameworks and fields and improvement in sensory aspects

    Taking a big step with the development of AI, the functions of conversation agents are becoming increasingly mature, and people's lives are becoming increasingly dependent on the conversation agent system. Its assistance to people is reflected in many fields. This has sparked people's exploration of some emerging fields, it is necessary to organize and summarize the new technologies, functions, data, and methods for training new data required for studying emerging fields. More and more deep learning technology have been applied to conversation agents that improve the quality of the service significantly. It is still in the initial stage, and needs improvement both at modeling methods and datasets gathering.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230810

    A comprehensive analysis of gesture recognition systems: Advancements, challenges, and future direct

    Gesture recognition emerges as a potent avenue for human-computer interaction, harnessing mathematical algorithms to interpret gestures. It promises to surpass text-based or graphical interfaces, enabling touchless device control through simple gestures. Our review of 7 papers encompassing various fields and methods underscores its diverse applications. Challenges persist, such as distinguishing genuine user intent from accidental actions amid environmental interference. Creating a universal EMG pattern recognition model demands intricate individual pre-training. Sensor-based gesture recognition grapples with real-world dynamics, necessitating adaptable models that discern user intent from non-intent actions. Addressing these gaps holds the key. Adaptable models and personalized approaches can enhance robustness and accuracy across applications, surmounting challenges in the gesture interaction technology realm.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230811

    Review of collaborative filtering recommendation systems

    In the era of information overload, recommender systems develop rapidly. And because the needs of information consumers are full of diversity and the information data provided by information producers is too large, to enhance the efficiency and quality of recommendations, the research community has introduced numerous approaches to optimize recommendation systems. As collaborative filtering stands as a time-tested technique in recommendation systems, This paper facilitates a swift comprehension of recent advances in collaborative filtering. It does so by examining the techniques presented across the entire collaborative filtering recommendation systems research field in recent years, especially its development in the domain of deep learning, and have a solid understanding of the field of study.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230812

    Should machine learning be applied in credit risk accessment

    In the past, analysts evaluated whether to offer loans to particular applicants using rule-based approaches. However, due to the sudden rise in applicants and a labor shortage, financial institutions have created quantitative methods of decision-making. Credit scoring models are constructed. In this essay, random forest model, support vector machine regression model and Probit model are performed and compared according to the dataset from a major U.S. credit cards company. The result demonstrates that while machine learning techniques can improve the efficiency and accuracy of credit risk assessment, it does face some problems and limitations. Random forest model is capable of handling high-dimensional data and is not complicated to run. However, database with fewer features or samples will have lower classification accuracy. Support vector machine regression model has high accuracy and prevents overfitting to some degree. It is sensitive to the choice of kernel parameters and regularization term. By testing how important Mill Ratio is, the Probit model produces more accurate results. However, the model is more complex than the other two. In future research, we propose to enhance and extend our work by using more artificial intelligence algorithms and evaluation metrics.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230813

    Breast cancer classification based on hybrid machine learning model

    This study proposes a hybrid model that combines K-Means clustering and Random Forest classification as an approach for breast cancer classification. The objective is to exploit the advantages of unsupervised clustering and supervised classification techniques to enhance the accuracy and robustness of classification models. The dataset underwent preprocessing procedures encompassing the handling of missing values, feature normalization, and feature selection. Missing values were addressed through appropriate methods, and features were scaled and selected based on variance threshold or correlation analysis. Subsequently, K-Means clustering was applied to the preprocessed data to assign cluster labels to each sample. The study then proceeded to train a Random Forest classifier by incorporating both the cluster labels and the raw gene eigenvalues as mixed features. This integration of gene expression values and cluster labels provides supplementary information to the classifier, enabling the capture of more intricate patterns within the data. The Random Forest classifier was trained using optimized parameters determined through parameter tuning, including the number of trees, maximum depth, and minimum number of split samples. Extensive experiments and evaluations conducted in this study revealed that the hybrid model outperformed the standalone Random Forest classification. The incorporation of K-Means clustering facilitated the discovery of underlying data structures and patterns, ultimately enhancing the classifier's discriminatory ability. The hybrid model exhibited superior accuracy, precision, recall, and F1 scores, demonstrating its efficacy in accurately classifying breast cancer samples.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230815

    The metamorphosis of machine translation: The rise of neural machine translation and its challenges

    Machine translation refers to the process of using computers to translate source language into target language, which has undergone significant transformations since its inception, with the current mainstream neural machine translation achieving satisfactory translation performance. This paper overviews the three developmental stages of machine translation: rule-based machine translation, statistical machine translation, and neural machine translation, with a focus on neural machine translation. It introduces the key models that emerged in the development process of neural machine translation, namely the recurrent neural network encoder-decoder model, recurrent neural network search model, and Transformer, and compares their strengths and limitations. Other relevant technologies and models developed alongside neural machine translation are also discussed. Addressing the current challenges of neural machine translation, the paper delves into issues of overfitting, low-resource translation, structural optimization of Transformer models, and enhancement of neural machine translation interpretability. Finally, the paper explores the prospects of applying neural machine translation to multimodal translation.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230816

    Current study on interaction design in automotive intelligent cockpit

    In recent years, with the rapid development of Internet communication digital technology represented by artificial intelligence, Internet of Things, big data, the human-computer interaction of automobiles has also been further developed, showing a new look. As the main part of automotive interaction design, the digital, intelligent and Internet-oriented characteristics of intelligent cockpit are becoming increasingly obvious, especially in recent years, the intelligent cockpit design of new energy vehicles has broken the traditional interaction mode of automobile cockpit in the era of industrial machinery, and more intelligent and immersive interaction design is adopted. And the cockpit of the car not only has a single function for driving, but also provides a space for leisure and entertainment for the driver. Key technologies and design cases of cockpits and its interaction design were sorted out and summarized, and then the problems that cockpits interaction design faces and its future development were analyzed in this paper.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230817

    Model-based reinforcement learning for service mesh fault resiliency in a web application-level

    Microservice-based architectures enable different aspects of applications to be created and updated independently, even after deployment. Associated technologies such as service mesh provide fault resiliency through attribute configurations that govern self-adaptive application-level behavior in response to failures, in a manner transparent to the application and constituent microservices. While this provides tremendous flexibility, the configured values of these attributes – and the relationships among them – can significantly affect the performance and fault resilience of the overall application. It is thus important to perform fault injection and load testing on the application, prior to full deployment. However, given a large number of possible attribute combinations and the complexities of the distributed system underlying microservices and service mesh architectures, it is virtually impossible to determine through traditional software development practices the worst combinations of attribute values and load settings with respect to self-adaptive application-level fault resiliency. To this end, we present a model-based reinforcement learning approach that determines the combinations of attribute and load settings that result in the most significant fault resilience behaviors at an application level. We validate our approach through a case study on a simple “request-response” service using the Istio service mesh. Our analysis shows that, even for a simple service, our model-based reinforcement learning approach outperforms a baseline selection of action parameters. Further, we show that communicative multi-agent reinforcement learning improves the performance of both the non-communicative single and multi-agent learning paradigms.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230819

    Automated arduino robot design with multi-level natural language processing chains

    This research explores the potential of leveraging OpenAI’s GPT-3.5-Turbo API for automating Arduino robot design through a structured multi-level language processing chain termed “LangChain.” The system breaks down the design process into six stages, from preliminary design sketching to failure analysis. Each stage generates robot design components using user inputs, progressively building upon the previous outputs. The results highlight the model’s capabilities in generating functional preliminary designs, suggesting hardware and software components, and offering assembly instructions. While demonstrating a commendable level of technical knowledge, the system requires further refinement in numerical analysis and design reviews. This approach provides a foundational framework for bridging the knowledge gap between amateurs and professionals in robotic design.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230822

    The design of relational database and NoSQL database in travel agency database system

    The information management systems of travel companies are experiencing greater challenges and expectations as a result of the tourism industry’s rapid growth and travellers’ rising demand for personalized and interactive experiences. This paper aims to study the customer management system of travel agencies, taking into full consideration the characteristics and development trends of the tourism industry. Traditional customer management system uses relational database for data storage and related operations, which cannot fully meet the needs of customers and complex data in the new era. By analysing and comparing relational databases and NoSQL databases, according to their own advantages, a combined solution is proposed, which uses relational databases to store structured data and NoSQL databases to store unstructured data. On the basis of maintaining the original system management function, this solution greatly enhances the storage and processing of interactive data such as images and videos, and better meets the new demand of travel agency customers for sharing and interactive communication.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230823

    Research on challenges and solutions in 5G application

    5G technology is a new type of wireless communication technology with the characteristics of high speed, low delay, large connection, etc. It brings new opportunities and challenges to economic, social and industrial development. This article focuses on the application and value of 5G technology in smart cities, as well as the opportunities and challenges faced by 5G technology in smart cities. This article uses the method of literature reading and analysis to explore the role and functions of 5G technology in smart cities from the fields of smart homes, smart transportation, smart grids, smart security, etc. and analyzes the application scenarios and cases of 5G technology in smart cities, demonstrating the practical effects and social benefits of 5G technology in smart cities. This article evaluates the opportunities and challenges faced by 5G technology in smart cities, such as policy support, network construction, security, user needs, etc., and provides strategic suggestions for the promotion and application of 5G technology in smart cities. This article aims to improve the knowledge and understanding of 5G technology and smart cities, promote the coordinated development of 5G technology and smart cities, and provide technical support and innovation power for building a new digital, networked, and intelligent city.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230824

    Research about functions, fabrications and applications of the microstructures in sensors

    Microstructures in sensors are of immense importance in technology and scientific research. Microstructures play a vital role in achieving enhanced sensitivity, accuracy, and miniaturization in sensors. Understanding their importance is crucial for advancing sensor technology in healthcare, environmental monitoring, robotics, and other fields. To conduct this research, a systematic review of academic and industry sources is performed. Existing studies, experimental techniques, and real-world applications are analyzed. The paper covers diverse topics related to microstructures in sensors. It highlights the importance of microstructures, explores types such as Micro-electromechanical Systems (MEMS), and nanomaterials, and discusses their unique characteristics. Fabrication techniques like lithography, thin-film deposition, and additive manufacturing are examined. In addition, the advantages and limitations of each method are discussed, emphasizing manufacturing challenges and opportunities. Additionally, the paper explores applications of microstructures in biomedical sensing, environmental monitoring, consumer electronics, and industrial automation. Existing research and success stories showcase their potential to revolutionize various sectors.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230825

    Research on the application of personalized recommendation

    With the development of the Internet, people have more opportunities to be exposed to personalized recommendations, and the heat of related topics has increased. The purpose of this paper is to summarize the application principle and use of personalized recommendations by analyzing the relevant data, so that people can have a better understanding of personalized recommendations and make preparations for future related research. This paper first introduces the general development context of the personalized recommendation system and its uniqueness. The analysis finds that personalized recommendations can provide users with the most relevant and valuable information, goods, or services according to their interests, preferences, and behaviors. Secondly, this paper explores the application scenarios of personalized recommendation systems in different fields, including but not limited to video content, e-commerce, and online learning. In addition, this paper also lists the application of YouTube and Netflix in video content recommendation, as well as personalized recommendation services in e-commerce and online learning platforms. In particular, the rise of intelligent adaptive online learning systems and their adaptability in the field of education and training are analyzed in detail. Through this research, it can be found that a personalized recommendation system is very beneficial to people's production and life, but at present, personalized recommendation is still in the development stage, and there are many problems to be solved.

  • Open Access | Article 2024-02-26 Doi: 10.54254/2755-2721/43/20230826

    The development and application of autonomous driving technology

    Autonomous driving technology is emerging as a prominent subject within the realm of modern technology. This technology is increasingly being applied to daily life and is seen as an innovative technology with enormous potential. The application of autonomous driving technology has expanded but still faces issues such as immature technology, incomplete laws, and low user acceptance. The objective of this paper is to comprehensively examine the developmental trajectory, application domains, and obstacles faced by this technology, as well as to anticipate its prospects. To that end, this paper initially provides an overview of the background and relevant concepts pertaining to autonomous driving technology. Subsequently, the developmental history of autonomous driving technology, along with its current application domains, is analyzed. Moreover, the challenges encompassing technical, legal, and ethical aspects of autonomous driving technology are thoroughly explored. Lastly, this paper offers prognostications regarding the future progress of autonomous driving technology, as well as furnishes recommendations for advancing its implementation. It is found that although the research on autonomous driving has been very popular in the past few years, there is still a lack of research on the application and challenges of autonomous driving technology in some fields. Therefore, this paper summarizes the application in different fields, hoping to propose a general direction for the application of autonomous driving in the future.

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