A dynamic mutation probability formula is utilized to optimize the model. In order to solve the logistics warehouse path problem, the ant colony optimization algorithm, optimized by a genetic... Show More

A dynamic mutation probability formula is utilized to optimize the model. In order to solve the logistics warehouse path problem, the ant colony optimization algorithm, optimized by a genetic algorithm, is employed to construct a logistics warehouse path optimization model. This model effectively optimizes the logistics warehouse paths. Test results comparing the convergence and distribution of non-inferior solutions demonstrated that the proposed model outperforms others in terms of convergence and non-inferior solution distribution. In practical logistics warehouse optimization, applying the proposed model to optimize cargo locations can significantly enhance the effectiveness of the objective function. The optimization resulted in improvements for all four objective functions related to cargo location, with reduction rates of 10.38%, 30.88%, 51.78%, and 88.49%, respectively. For the optimization of logistics warehouse paths, the original distance was 47.6m, which was reduced to 27.8m after optimization. Consequently, the picking distance decreased by 41.60%.

With the advent of the information age, the traditional pavement management technology of operating expressways can no longer meet the higher requirements for the improvement of engineering quality... Show More

With the advent of the information age, the traditional pavement management technology of operating expressways can no longer meet the higher requirements for the improvement of engineering quality in the information age. This paper proposes a method of integrated analysis based on BIM (building information modeling) and GIS (geographic information system), builds an intelligent platform for highway operation and maintenance, and solves the problem of data islands in highway maintenance and management.

ICAO has emphasized that aeronautical information agencies should provide digitized aeronautical data and information, and realize that aeronautical data exchanging internationally in AIM. The AIXM... Show More

ICAO has emphasized that aeronautical information agencies should provide digitized aeronautical data and information, and realize that aeronautical data exchanging internationally in AIM. The AIXM structured aeronautical information dataset will be the main source of aeronautical basic data in the aeronautical information exchange network. In this article, the authors first analyze the spatio-temporal attributes of AIXM dataset and design the query method of AIXM structured obstacle data based on the research of AIXM coding specification. Secondly, the helicopter path planning is taken as the research scenario. Using the AIXM obstacle dataset and route dataset, combining the helicopter performance constraints to construct the envelope frame for collision judgment, and a new path planning method with improving the classical A* algorithm based on the AIXM dataset is proposed. The proposed method is validated and visualized. The validation results show that the proposed method reduces the frequency of helicopter turning, and ensures the safe distance between the flight path and the obstacles.

The green credit policy has significantly influenced the growth of green industries in China. This study evaluates its impact on reducing bank credit risk using data from 26 Chinese banks from 2015... Show More

The green credit policy has significantly influenced the growth of green industries in China. This study evaluates its impact on reducing bank credit risk using data from 26 Chinese banks from 2015 to 2021. The authors discovered that the policy's primary effect is linked to banks' financial leverage. Notably, green credit's influence on insolvency risk is most evident in leverage risk. However, despite governmental support for green credit collaboration, prevalent information gaps between banks and green enterprises lead to misjudgments and subsequent credit losses. To address the balance between credit risk mitigation and privacy, the authors investigated vertical joint learning for a precise risk control model grounded in commercial banks' practices. Experiments revealed that this joint model outperforms the sole “bank internal model” in presenting green credit data, underscoring the potential of machine learning to refine green credit systems and bolster banks' credit risk management.

The Internet's rapid growth has led to information overload, necessitating recommender systems for personalized suggestions. While content-based and collaborative filtering have been successful... Show More

The Internet's rapid growth has led to information overload, necessitating recommender systems for personalized suggestions. While content-based and collaborative filtering have been successful, data sparsity remains a challenge. To address this, this article presents a novel social recommender system based on convolutional neural networks (SRSCNN). This approach integrates deep learning and contextual information to overcome data sparsity. The SRSCNN model incorporates user and item factors obtained from a neural network architecture, utilizing features from item titles and tags through a CNN. The authors conducted extensive experiments with the MovieLens 10M dataset, demonstrating that the SRSCNN approach outperforms state-of-the-art baselines. This improvement is evident in both rating prediction and ranking accuracy across recommendation lists of varying lengths.

Support vector data description (SVDD) inspires us in data analysis, adversarial training, and machine unlearning. However, collecting support vectors requires pricey computation, while the... Show More

Support vector data description (SVDD) inspires us in data analysis, adversarial training, and machine unlearning. However, collecting support vectors requires pricey computation, while the alternative boundary selection with O(N2) is still a challenge. The authors propose an indispensable edge pattern selection method (IEPS) for data description with direct SVDD model building. IEPS suggests a double local analysis to select the global edge patterns. Edge patterns belong to a subset of the target problem of SVDD and its variants, and neighbor analysis becomes pivotal. While an excessive number of participating data result in redundant computations, an insufficient number may impede data separability or compromise the model's quality. Consequently, a data-adaptive sampling strategy has been devised to ascertain an optimal ratio of retained data for edge pattern selection. Extensive experiments indicate that IEPS keeps indispensable edge patterns for data description while reducing the interference in the norm vector generation to guarantee the effectiveness for clustering analysis.

This paper aims to explore Kuwaiti users' perspectives and experiences with Shlonik, the mandatory contact tracing application that was used during the COVID-19 pandemic. A sample of 2,450 tweets... Show More

This paper aims to explore Kuwaiti users' perspectives and experiences with Shlonik, the mandatory contact tracing application that was used during the COVID-19 pandemic. A sample of 2,450 tweets about users' experiences was collected and thematically analyzed. The analysis used DeLone and McLean's information system success model to explore aspects of the degree to which this app can be judged a “success,” using parameters of system quality and service quality. A novel finding identified by this qualitative study was the significance of themes identified in the analysis such as social privacy (timing, privacy, and female privacy, which are related to the cultural and religious norms of Kuwait) and technical privacy (related to data protection). The research identified significant conflict with cultural and social norms in users' experience of the Shlonik app – factors not normally identified or discussed in existing literature. This research supports a need for improved strategies and designs in future m-government applications in developing countries.

In this article, we discuss the stagnation problem related to circulating digital course resources (DCRs) in China. Using information ecology theory, questionnaires and practical tests, we learn... Show More

In this article, we discuss the stagnation problem related to circulating digital course resources (DCRs) in China. Using information ecology theory, questionnaires and practical tests, we learn that universities in China are embodied with information isolation, and hence circulating the associated DCRs is inhibited. Our results show that the original simple mechanical construction no longer meets the current needs of teaching and learning. Consequently, coordination is essential among relevant subjects and circulating DCRs. In doing so, we further reoptimise the current circulating system of DCRs in Chinese universities through the use of information ecology theory and in turn provide a new solution for the sustainable construction and application of DCRs.

Traditional customer segmentation methods cannot obtain more effective information from massive customer data, which affects the formulation of marketing strategies. Based on this, this study... Show More

Traditional customer segmentation methods cannot obtain more effective information from massive customer data, which affects the formulation of marketing strategies. Based on this, this study constructs a customer segmentation marketing strategy model that integrates support vector machines and clustering algorithms. This model first utilizes support vector machines to segment existing customer data, and then integrates support vector machines and clustering algorithms to construct a customer segmentation model. Finally, simulation experiments are conducted using the dataset. The results show that the model algorithm obtains the optimal solution when the quantity of iterations is 50. Meanwhile, the average error rate of the model algorithm in the customer segmentation process is 6.82%, the average recall rate is 91.28%, and the average profit predicted by the impact strategy developed by the segmentation model is 29.88%, which is 2.53% different from the true value.

In the geographical field, the studies have been on service areas using the Voronoi diagram and its derived models is extensive, but there is a lack of effective methods to achieve a good... Show More

In the geographical field, the studies have been on service areas using the Voronoi diagram and its derived models is extensive, but there is a lack of effective methods to achieve a good area-weight proportionality between generators and their exclusive regions. As a famous visualizing method, adaptive multiplicatively weighted Voronoi diagrams are able to achieve it, but are limited to displaying non-spatial data. The approach of the area-weight proportional multiplicatively weighted Voronoi diagram is proposed to solve these problems by allowing for spatial division with a point-fixed iteration approach and a vector-based multiplicatively weighted Voronoi diagram construction method from point features with spatial coordinates and references in GIS environments. It enables one to create a set of regions that is proportional to the weights of the generators. The method is successfully tested on a series of cases. The approach aims to establish a kind of spatial data model to represent demand and supply situations in real life.

Enterprise resource planning (ERP) implementations often encounter multifaceted challenges, leading to failures. Success relies on technical competence, management support, and user engagement.... Show More

Enterprise resource planning (ERP) implementations often encounter multifaceted challenges, leading to failures. Success relies on technical competence, management support, and user engagement. Unique organizational processes significantly influence outcomes. Failures can disrupt operations, highlighting the need to identify critical success factors (CSFs) for effective ERP implementation. This study employed analytic hierarchy process (AHP) methodology to analyze CSFs. Data collection involved surveys administered to a social insurance company's ERP project team in Indonesia. This study revealed 15 success factors, categorized into organization, process, and technology dimensions. Organization emerged as the most crucial, followed by technology and process. Among these, five CSFs stood out: project team competence, vendor and consultant quality, ERP fit, top management support, and hardware and software selection.

When a cyber incident occurs, organizations need to identify the attack's impacts. They have to investigate potentially infected devices as well as certainly infected devices. However, as an... Show More

When a cyber incident occurs, organizations need to identify the attack's impacts. They have to investigate potentially infected devices as well as certainly infected devices. However, as an organization's network expands, it is difficult to investigate all devices. In addition, the cybersecurity workforce shortage has risen, so organizations need to respond to incidents efficiently with limited human resources. To solve this problem, this paper proposes a tool to assist an incident response team. It can visualize ATT&CK techniques attacker used and, furthermore, detect lateral movements efficiently. The tool consists of two parts: a web application that extracts ATT&CK techniques from logs and a lateral movement detection system. The web application was implemented and could map the collected logs obtained from an actual Windows device to the ATT&CK matrix. Furthermore, actual lateral movements were performed in an experimental environment that imitated an organizational network, and the proposed detection system could detect them.

In the realm of physical education in college, the traditional classroom teaching approach primarily relies on paper textbooks and demonstrations by the teacher. However, in today's physical... Show More

In the realm of physical education in college, the traditional classroom teaching approach primarily relies on paper textbooks and demonstrations by the teacher. However, in today's physical education classrooms, there is a need to change these traditional teaching methods. Utilizing multimedia technology and interactive interfaces, teachers can demonstrate the essential actions and intricate details through multimedia videos and other formats such as push-pull and shaking. This chapter introduces a multi-segment human body tracking algorithm that focuses on real-time tracking. The human body target is divided into multiple segments, and an online learning method based on Hough Forests is used to learn the overall appearance of the human body and the appearance model of each segment. The research findings indicate that when the merged area exceeds 82.36% of the pre-segmentation area using watershed analysis, smaller areas lead to faster segmentation speed, while larger areas result in slower segmentation speed. Compared to other methods, this approach yields better recognition results.

Implementing mental health education (MHE) in schools is vital for students' psychological well-being. Subject infiltration integrates psychological factors into subject learning, promoting healthy... Show More

Implementing mental health education (MHE) in schools is vital for students' psychological well-being. Subject infiltration integrates psychological factors into subject learning, promoting healthy development. However, English learning can introduce issues like self-confidence and language anxiety. Early identification of mental health problems (MHP) is crucial. This study, rooted in humanistic psychology and constructivism, explores affective factors, strategies, teaching, and English listening. The algorithm, based on the DeepPsy model, shows promise, identifying 75% of students with MHP. This research aids universities in offering timely support to high-risk students, minimizing long-term harm. The algorithm contributes to a healthier learning environment by enhancing English teaching and addressing mental health issues early.

With the continuous progress of information technology, distance English teaching is becoming a practical choice. The introduction of 5G technology has improved the English classroom experience and... Show More

With the continuous progress of information technology, distance English teaching is becoming a practical choice. The introduction of 5G technology has improved the English classroom experience and provided innovation for modern teaching. With the help of wireless communication technology, teachers can effectively impart cognitive skills. Compared with traditional English teaching methods, it obviously enhances the two-way communication between students and professors. Teaching students in accordance with their aptitude uses the reformed Best Available Technology Optimization Algorithm (RBOA) to optimize the transmission process and evaluate students' cognitive ability. This study shows that the proposed method seems to be more effective than the traditional college English course and can significantly improve students' language ability. This optimization scheme has a potential wide application prospect in teaching practice, which has injected new vitality and possibility into English education.

This article conducts a series of research on computer network courses based on “flipped classroom.” Teaching reform will be carried out for computer networks, but the results are mediocre. The... Show More

This article conducts a series of research on computer network courses based on “flipped classroom.” Teaching reform will be carried out for computer networks, but the results are mediocre. The author of this article has carefully considered and summarized computer network teaching based on his years of teaching experience and has put forward suggestions for widespread application. This article discusses the application of translation. The necessity and connotation of transitioning to classroom mode were emphasized, and the design of this teaching method was emphasized, adopting a three-step approach of “consolidating and improving knowledge after class.” Therefore, teachers should start from their own perspective, based on process preview, in class learning, and after class review, find the connection point between flipped classroom and computer network courses, reflect on the problems in teaching, and adopt targeted teaching methods to solve problems. This article provides guidance for improving teachers' teaching level.

These days, the problem of fake news has grown to be a major social and personal concern. With the amount of information generated through social media, it is very crucial to be able to detect and... Show More

These days, the problem of fake news has grown to be a major social and personal concern. With the amount of information generated through social media, it is very crucial to be able to detect and properly take care of that fake information. Previous studies proposed a machine learning model to detect fake news in online Thai health and medical articles. Still, the problem of detecting fake news with similar content but different objectives exists, and the accuracy of the model needs improvement. Therefore, this study aims to solve these problems by adding 33 new features, including textual features, sentiment-based features, and lexicon features, i.e., herbs, fruits, and vegetables, to identify the objective of an article. We trained and tested the model's prediction accuracy on a new dataset containing 582 reliable and 435 unreliable (fake news) articles from eight Thai websites. Our improved classification model using XGBoost with Lasso, the best feature selection method, achieved an accuracy of 97.76% without over-fitting, reflecting a 7.16% improvement over our earlier model.

The dynamic deontic (DD) is a norm synthesis framework that extracts normative rules from reinforcement learning (RL), however it was not designed to be applied in agent coordination. This study... Show More

The dynamic deontic (DD) is a norm synthesis framework that extracts normative rules from reinforcement learning (RL), however it was not designed to be applied in agent coordination. This study proposes a norm augmented reinforcement learning framework (NARLF) that extends said model to include a norm deliberation mechanism for learned norms re-imputation for norm biased decision-making RL agents. This study aims to test the effects of synthesized norms applied on-line and off-line on agent learning performance. The framework consists of the DD framework extended with a pre-processing and deliberation component to allow re-imputation of normative rules. A deliberation model, the Norm Augmented Q-Table (NAugQT), is proposed to map normative rules into RL agents via q-values weight updates. Results show that the framework is able to map and improve RL agent's performance but only when synthesized off-line edited absolute norm salience value norms are used. This shows limitations when unstable salience norms are applied. Improvement in norm extraction and pre-processing are required.

With the increasing of computing and network technology, there has been a rise in children's educational and entertainment software. However, these software applications depend on mouse and keyboard... Show More

With the increasing of computing and network technology, there has been a rise in children's educational and entertainment software. However, these software applications depend on mouse and keyboard input, which may be tedious and challenging for children. Moreover, these applications fail to capture the interaction between users and computers. This study proposes design principles for children's human-computer interaction based on children's cognitive, psychological, and physiological attributes, and outlines a framework accordingly. The framework integrates natural human-computer interaction technologies, such as physical operation interface, gesture interaction, low-level semantics-based human-computer interaction, and high-level semantics-based human-computer interaction, with the aim of facilitating seamless and intuitive interaction between children and computers. Finally, the results demonstrating the feasibility and effectiveness of the proposed human-computer interaction technology as a child-centered virtual reality application tool.

While some research has been conducted to identify the strengths and weaknesses of Small Private Open Courses (SPOCs), a significant research gap exists in comprehensively examining the... Show More

While some research has been conducted to identify the strengths and weaknesses of Small Private Open Courses (SPOCs), a significant research gap exists in comprehensively examining the opportunities and threats they face. To determine if SPOCs are beneficial to learners, the authors adopted the PRISMA protocol to filter literature for SWOT analysis in SPOCs. The authors identified and analyzed the strengths, weaknesses, opportunities and threats faced by SPOCs using a SWOT analysis method. The strengths of SPOCs include high-quality content and course delivery, accessibility, and personalization. Weaknesses involve lack of social interaction, limited resources, credentialing issues. Their opportunities consist of market growth, collaboration with other institutions, and access to new student markets. Their threats include competition from traditional courses, technology updates, and student privacy concerns.

The need to improve corporate environmental performance has emerged as a crucial and urgent study topic as the world's pollution and climate challenges get worse. This trend has taken on new meaning... Show More

The need to improve corporate environmental performance has emerged as a crucial and urgent study topic as the world's pollution and climate challenges get worse. This trend has taken on new meaning and importance in light of the expanding digital economy and digital transformation. Using financial and associated data from 2009 to 2021 as the sample, this research empirically studies the link between digital transformation, green innovation, and environmental performance with a focus on China's A-share listed enterprises. According to the study's findings, digital transformation may improve a company's environmental performance by strengthening its capacity for green innovation. Furthermore, heterogeneity analysis shows that the effect of digital transformation in enhancing environmental performance is more prominent in high-tech enterprises, non-heavy polluting companies, and organizations with a greater percentage of party members in senior management.

Based on big data mining and learning analysis technology, this paper systematically summarizes the theoretical basis of network-based college students' English writing skills training mode and... Show More

Based on big data mining and learning analysis technology, this paper systematically summarizes the theoretical basis of network-based college students' English writing skills training mode and discusses the reform and application of college English writing teaching. By mining network data, establishing corresponding writing training database and writing corpus, and at the same time strengthening writing teaching methods with the help of existing network corpus. Students learn through reading, imitation and innovation in the course, and receive psychological and motivation training. The research shows how data mining ability can test the data production ability of front-line authors, and test the ability of front-line teachers to cultivate students' autonomous learning and self-adaptation. It is emphasized that this teaching mode updates the knowledge structure of front-line teachers and cultivates new teachers who combine knowledge with technology. By providing innovative methods to optimize the teaching process, it is expected to effectively improve students' writing ability.

Exploring how to utilize images to enrich music teaching content and provide a more visually impactful learning experience is an important topic. Therefore, this paper introduces a convolutional... Show More

Exploring how to utilize images to enrich music teaching content and provide a more visually impactful learning experience is an important topic. Therefore, this paper introduces a convolutional neural network-based algorithm for extracting audio features to construct a music visualization model. By identifying features such as note pitches, it enhances pitch recognition and integrates with CNN algorithms for audio information visualization. Experimental results demonstrate an accuracy rate exceeding 97%, showcasing the significant advantage of this method in visualizing audio information in music multimedia classrooms. It provides technical support for bringing a new visual experience to music education.

This study explores the use of an enhanced Support Vector Machine (SVM) algorithm to address challenges faced by students with lower academic performance in college English education, particularly... Show More

This study explores the use of an enhanced Support Vector Machine (SVM) algorithm to address challenges faced by students with lower academic performance in college English education, particularly in integrating ideological and political education. It develops a dynamic early warning system to provide timely support, employing targeted English courses and practical teaching methods. Methodologically, an improved SVM algorithm constructs a robust early warning model, enhancing monitoring and support in college English courses. The system's application contributes to advancements in machine learning and AI, emphasizing data-driven decision-making in education. Future research could explore scalability, long-term impacts, and further refinements to the SVM algorithm. In summary, the study successfully applies machine learning techniques to devise an innovative dynamic early warning system for English ideological and political education in college, offering valuable insights for practitioners and researchers alike in the realm of AI-assisted pedagogy.

How to use big data technology to promote the integration and complementarity of English online education and offline education has become an urgent problem in the field of higher education. This... Show More

How to use big data technology to promote the integration and complementarity of English online education and offline education has become an urgent problem in the field of higher education. This paper mainly studies the integration and complementary strategies of online and offline English education in higher vocational colleges based on big data technology. This paper first analyzes the current situation of English teaching in higher vocational colleges, then discusses the application of big data technology in online English education in higher vocational colleges and puts forward relevant strategies of integration and complementarity. Finally, it expounds the important role of big data technology in promoting the integration and complementarity of online and offline English education in higher vocational colleges. In the future, we can further strengthen the application depth and breadth of big data technology in higher education, so as to promote the reform and development of English teaching in higher vocational colleges and improve the teaching quality.

Traditional economic theory revolves around information asymmetry, where some possess more knowledge than others. However, the digital age is upending this paradigm. This research proposes a... Show More

Traditional economic theory revolves around information asymmetry, where some possess more knowledge than others. However, the digital age is upending this paradigm. This research proposes a groundbreaking shift: a transition from an Asymmetric Information Economy to a Symmetric Knowledge and Wisdom Economy, fueled by the ubiquitous availability of data and digital communication. The shifting is worked through DIKWP-12 Chains framework (Data-Information-Knowledge-Wisdom-Purpose), which serves as a key pillar supporting this paradigm shift. It integrates Data, Information, Knowledge, Wisdom, and Purpose into a unified semantic processing approach. This novel approach empowers us to navigate this transformative era. Furthermore, this study goes beyond theory by leveraging the DIKWP chains to construct an Intelligent Consultant System for Real Estate Investment. This system analyzes customer behavior data and captures the wisdom behind their purchasing decisions, offering a unique and data-driven approach to real estate investment consulting.

Library resource library management based on modern information technology and reconfigurable mobile information systems have attracted much attention from researchers of academia. This paper... Show More

Library resource library management based on modern information technology and reconfigurable mobile information systems have attracted much attention from researchers of academia. This paper introduces the LSTM model into the field of Chinese library resource management, and tries to propose a classification system with practical application value. The main research work:The Chinese library resource is designed LSTM model, and three experiments are designed on this basis: the coarse-grained book single classification experiment, different measures on the classification results; the fine-grained book single classification experiment, to compare The advantages and disadvantages of direct classification and layer-by-layer classification; coarse-grained book multi-classification experiments to explore the feasibility of multi-classification. After analyzing the experimental results, the model has achieved a good classification effect, and overcomes some deficiencies in the library resource library predecessors, and has a strong practical application value.

The traditional entrepreneurial resource decision-making model that relies on empirical decision-making or simple template matching is difficult to adapt to the current complex social environment.... Show More

The traditional entrepreneurial resource decision-making model that relies on empirical decision-making or simple template matching is difficult to adapt to the current complex social environment. Therefore, the multi-objective grey wolf algorithm (MOGWO) is used to solve the Pareto frontier of the problem model, replacing the optimal solution with the optimal solution set, and then selecting the optimal scheduling plan according to the actual situation, so as to make the decision-making plan more scientific and reasonable. In order to optimize this algorithm, two improvement strategies are proposed on the basis of analysing the movement of individual grey wolves. The research in this paper provides an important reference for the machine learning algorithm and the improved multi-objective grey wolf algorithm. The experimental results show that the MOGWO algorithm can overcome the shortcomings of the basic grey wolf algorithm (GWO) in terms of insufficient exploratory ability and local convergence, and has higher search efficiency, better optimality finding ability and stability.

In today's modern environment, as an important part of stage art and performance, many places need to use stage lighting. Therefore, the correct use of stage lighting design is particularly... Show More

In today's modern environment, as an important part of stage art and performance, many places need to use stage lighting. Therefore, the correct use of stage lighting design is particularly important, and it is necessary to study the design of stage lighting. However, the traditional stage light projection is designed by the designer according to the understanding of music and music matching light projection. This scheme is easily influenced by the designer's subjective factors, which lack a clear and perfect design scheme. In this context, from the perspective of holographic naked eye 3D, this paper designs from the stage layout, utilization rate of lighting, the analysis of music features and the distribution of different colors, and evaluates the merits and disadvantages of music and light shows. This evaluation method can provide a design basis for the light projection in the design process of art stage and provide a scientific basis for the evaluation method of performance scheme. It can greatly reduce the design and debugging time of the scheme and save the design cost of the lighting projection.

Online learning (OL) is a new platform, which is used as a supplementary resource to improve the teaching and learning process in university education. The open educational resources provide... Show More

Online learning (OL) is a new platform, which is used as a supplementary resource to improve the teaching and learning process in university education. The open educational resources provide supplementary resources for strengthening online learning platform. In this work, an artificial intelligence-based online learning platform is designed that can manage resources using scheduling technology. The OL platform automatically allocates resources through appropriate cluster-based scheduling technology. The proposed deep learning-based OL platform (DLOL) supports the sequential integration of the model by simplifying and adjusting the model operating environment. Combined with the fuzzy-based convolutional neural networks, the decision tree algorithm is used to implement OL platform using blockchain models. The research results proved that artificial intelligence can help students improve their learning ability and makes the learning process relatively easy and exciting.

An emerging landscaping and lawn care service company (GreenLawn) is seeking to transition from manual record-keeping to digital solutions. With significant growth over two years, GreenLawn requires... Show More

An emerging landscaping and lawn care service company (GreenLawn) is seeking to transition from manual record-keeping to digital solutions. With significant growth over two years, GreenLawn requires efficient software to manage operations and finances while avoiding complexity and extensive training costs. The company evaluates Microsoft Excel and Google Sheets, weighing features, collaboration capabilities, ease of use, and pricing. GreenLawn's specific needs include service scheduling, workforce management, client information storage, inventory tracking, marketing, expense tracking, and financial modeling. The comparison highlights Excel's advanced features and integration with Microsoft Office versus Google Sheets' real-time collaboration and cost-effective accessibility. Questions address software selection, task management, collaboration strategies, cost-effectiveness, integration with industry tools, scalability, training, and adoption. Ultimately, GreenLawn's decision hinges on aligning software capabilities with its unique requirements and growth objectives.