Generative AI has quickly become a cornerstone of modern software development and IT support. Unlike traditional AI systems, which require explicit programming for each task, generative AI models learn from vast datasets to produce human-like text, code, and design elements (e.g. UI/UX design, graphic design). This capability is driving a new wave of productivity and innovation across the IT industry.Accelerating Software DevelopmentDevelopers are increasingly relying on generative AI assistants to streamline their workflows. Tools like GitHub Copilot can suggest entire functions or debug code in real time, reducing development time and minimizing errors. This shift allows development teams to focus more on problem-solving and creative design rather than repetitive coding tasks.Generative AI also helps bridge knowledge gaps by generating documentation, explaining complex code, and providing instant answers to developer queries. This democratizes software development, enabling junior developers to contribute more effectively and accelerating the onboarding process for new team members.Enhancing Technical SupportIn the realm of IT support, generative AI powers intelligent chatbots and virtual assistants that provide 24/7 helpdesk services. These AI agents can understand natural language, troubleshoot common issues, and escalate complex problems to human experts when necessary. This not only improves response times but also reduces operational costs and enhances user satisfaction.Additionally, generative AI can analyse support tickets and user feedback to identify recurring problems and suggest proactive fixes. This predictive capability helps IT teams maintain system reliability and improve service quality.Challenges and Ethical ConsiderationsWhile generative AI offers tremendous benefits, it also presents challenges. Ensuring the accuracy and security of AI-generated code is critical to prevent vulnerabilities. There are also concerns about intellectual property rights and the ethical use of AI-generated content. Organizations must implement robust review processes and maintain transparency when deploying generative AI tools. Additionally, there are concerns about bias in AI outputs and the risk of over-reliance on AI-generated content.ConclusionThe rise of generative AI is reshaping software development and IT support, offering new levels of efficiency and innovation. By understanding and responsibly implementing these technologies, IT professionals and organizations can unlock new opportunities and deliver exceptional value in today’s digital world.

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Rise of Generative AI in Software Development and Support

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The IT industry is experiencing a transformative era, thanks to rapid advancements in artificial intelligence. As we move through 2025, AI is not just an auxiliary tool—it’s at the heart of innovation, reshaping how organizations operate, secure their data, and interact with technology. Here are the top five AI-driven innovations currently making waves in the IT sector.&nbsp;1. Generative AI for Code and Content CreationGenerative AI models, such as OpenAI’s GPT-4 and Microsoft’s Copilot, are revolutionizing software development. These tools can write code snippets, generate documentation, and even create test cases, significantly boosting developer productivity. Beyond coding, generative AI is also used to automate report generation, create technical content, and assist in knowledge management, making it a versatile asset for IT teams.&nbsp;2. AI-Enhanced CybersecurityWith cyber threats growing more sophisticated, AI-powered security systems have become critical. These systems use machine learning to detect anomalies, predict attacks, and respond to threats in real time. AI-driven threat intelligence platforms analyze massive datasets to identify vulnerabilities and recommend proactive defences, helping organizations stay ahead of cybercriminals.&nbsp;3. AIoT (Artificial Intelligence of Things)AIoT differs from traditional IoT by incorporating artificial intelligence to enable local decision-making and edge computing. This allows for more efficient and autonomous operations in smart manufacturing and network management. AI algorithms process data at the edge, allowing devices to make decisions locally without relying on cloud connectivity. This reduces latency and improves efficiency in applications ranging from smart manufacturing to intelligent network management.&nbsp;4. AI-Powered IT Operations (AIOps)AIOps platforms leverage AI and big data analytics to automate and enhance IT operations. They help monitor system performance, predict outages, and optimize resource allocation. By automating routine tasks like incident management and root cause analysis, AIOps frees IT teams to focus on strategic initiatives and innovation.&nbsp;5. Quantum Computing and AI SynergyWhile still in its early stages, the combination of quantum computing and AI is opening new possibilities for solving complex problems that are beyond the reach of classical computers. In 2025, early quantum AI applications are being explored for optimizing logistics, drug discovery, and cryptography, signalling a new frontier for IT innovation. While quantum computing offers tremendous potential, it also faces limitations such as hardware constraints and the need for error correction. These challenges must be addressed to fully realize its benefits.&nbsp;ConclusionAI-driven innovations are transforming the IT landscape in 2025, offering unprecedented opportunities for efficiency, security, and growth. By embracing these technologies, organizations can stay competitive and prepare for the future of digital transformation.&nbsp;

Top 5 AI-Driven Innovations in IT for 2025

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Virtual Avatar Modelling & Animation in AR

The world has witnessed pandemics such as the 1918 Influenza [1], Ebola [2], SARS [3], H1N1 [4], MERS [3] etc. The COVID–19 pandemic is another pandemic we are witnessing, and it has infected millions of people all over the world. Millions of lives have been lost. As of 17 March 2024, the United States tops coronavirus cases with records of 103,436,209, including 1,182,424 deaths. On mortality count, Americas and Europe have accounted for 75.1% while there are only 6.0% of total deaths in Western Pacific containing China, Japan, South Korea, and Singapore [5]. The initial cases of this epidemic were discovered at a seafood market in Wuhan, China in late 2019, but there is no consensus about its origin. It quickly broke out into other parts of the world in early 2020 and then escalated to a pandemic. COVID–19 has interrupted civilization [6] for nearly three years. It has caused the most severe effects to the international society in comparison to the previous epidemics. Mask mandate was enforced. Social distancing was imposed. Large gathering was outlawed. Travel ban was imposed. Airports were deserted because planes were restricted to fly. Cruises were abandoned due to the fear of concentrated infection. Attractions lacked tourists because "unurgent and unnecessary" exits to foreign countries were restricted [7]. Classrooms were closed so students could only learn their classes online. Conference meetings were moved online. Cars were fewer on the roads. Polymerase chain reaction (PCR) tests and health QR codes [7] turned into mandatory prerequisite for residents to go outdoors. No one was free from the wrath of COVID–19 [6]. Successful containment strategies deployed in various countries have demonstrated the pivotal role of data analysis, scientific evidence, expert opinions, and depoliticization. For instance, Singapore implemented stringent "circuit breaker" [8] measures in 2020, but swiftly lifted restrictions in 2021 [9], because it cited a death rate of 0.2% [10], the lowest worldwide then. Meanwhile, China, in accordance with Zhong Nanshan et al.'s suggestions in 2020, enforced a rigorous lockdown in Wuhan [7, 11]. It was applied to other major cities across the nation to stop the virus from spreading farther as soon as possible. Despite its side effects on economy, tourism, education, international trade, and daily lifestyles, the zero–COVID–19 policy between 2020 and 2021 successfully limited mortality in China and made it a rare country with infrequent confirmed COVID cases. When Wuhan reopened on April 8, the lockdown was hailed as demonstration of "institutional advantage" by the government [12], especially compared to Western countries with massive deaths caused by their inaction [13]. However, the city–wide lockdown encountered its shortcomings during the Omicron outbreak in 2022 despite its success in 2020 [11]. Due to the complex administrative structure in China, the urgency of zero–COVID policy was exaggerated by junior officials, securities, and volunteers. Thus, they often implemented excessive epidemic prevention. For example, citizens were forced to take PCR tests in every 48 hours, sometimes in even 24 hours, to keep their QR codes green, otherwise they would be rejected from many buildings or communities by security. As all three doses of the vaccine were sufficient to saved lives from danger, many citizens complained that it was inconvenient and meaningless to maintain such strict policies while the virus has changed.This dichotomy has highlighted the importance for flexible policymaking levels to enhance epidemic response in large, multi–tiered governance systems. Countries like Singapore minimized the side effects of its epidemic prevention successfully and consistently since its decisions were made with respect of an advanced machine leaning model and precise data analysis evaluation. Scientists have carried out different studies to assist stakeholders in understanding the situation. The authors in [14] trained linear and polynomial models to predict case fatality rate of COVID–19 in Nigeria. In another study, scientists [15] discovered spatiotemporal transmission patterns of the epidemic in Hong Kong via deep learning. While classifying fatality rate serves as a basis for effective disease control in Singapore, it seems that machine learning articles on this task is in a paucity. Therefore, this study aims to design, develop, and evaluate a classification of COVID–19 fatality rate using learning algorithms. The proposed model will classify case fatality rate at national level into low, moderate, or high. &nbsp;2.Methodology 2.1. Data There are two datasets collected form WHO coronavirus (COVID–19) dashboard [5] and Our World in Data (OWID) [16] respectively. Features of both involves new cases, cumulative cases, new deaths, and cumulative deaths. The numbers of confirmed COVID–19 cases and deaths in the world are updated weekly by WHO [5]. For this study, only records from ten industrial countries are selected, as they contain only one missing value and one noisy value that can be handled well afterwards. These countries are China, Singapore, Korea, Japan, the USA, Canada, UK, France, Germany, and Russia. The data from OWID comprises daily cases and deaths in the USA, and it also provides their smoothed values. We select data between January 2020 to March 2023 for our objectives. Redundant and irrelevant features are removed during the data cleaning phase. We aimed to calculate and classify the case fatality rate using five learning algorithms. 2.2.Preprocessing. a. Interpolating Missing Values Given that our datasets contain both incremental and cumulative fields, a missing value can be easily estimated by values in adjacent rows and columns. b. Smoothing Noisy Values<img src="https://strapi.edps.com.hk/uploads/Screenshot_2025_06_30_171347_71ea74212d.png" alt="Screenshot 2025-06-30 171347.png"><img src="https://strapi.edps.com.hk/uploads/Screenshot_2025_06_30_171429_b13a7fe086.png" alt="Screenshot 2025-06-30 171429.png"><img src="https://strapi.edps.com.hk/uploads/Screenshot_2025_06_30_171502_6de75edb41.png" alt="Screenshot 2025-06-30 171502.png"><img src="https://strapi.edps.com.hk/uploads/Screenshot_2025_06_30_171529_ac1e4ed055.png" alt="Screenshot 2025-06-30 171529.png"><img src="https://strapi.edps.com.hk/uploads/Screenshot_2025_06_30_171614_c3a404d850.png" alt="Screenshot 2025-06-30 171614.png"><img src="https://strapi.edps.com.hk/uploads/Screenshot_2025_06_30_170627_984d04cb07.png" alt="Screenshot 2025-06-30 170627.png"><img src="https://strapi.edps.com.hk/uploads/Screenshot_2025_06_30_170710_2df89824a8.png" alt="Screenshot 2025-06-30 170710.png"><img src="https://strapi.edps.com.hk/uploads/Screenshot_2025_06_30_170743_f8e7d6a989.png" alt="Screenshot 2025-06-30 170743.png"><img src="https://strapi.edps.com.hk/uploads/Screenshot_2025_06_30_170812_6467dd3022.png" alt="Screenshot 2025-06-30 170812.png">5.Conclusion COVID–19 pandemic has resulted in major distraction to the society for nearly 4 years. It has infected millions of people and taken millions of lives. Aggressive containment strategies were vital when the virus performed high fatality rate, but they gradually brought major inconvenience to the society without being relaxed as the fatality rate dropped. Countries and regions such as Singapore have successfully implemented effective disease control by observing the case fatality rate within borders and categorize it for taking different measures. Therefore, in this study, we have designed, developed, and evaluated a COVID–19 fatality rate classifier, with datasets from World Health Organization and Our World in Data. Classification is based on low, moderate, or high. Imbalanced classes are equalized with SMOTE. a.In WHO's data, RFA and KNN perform the best for weekly and cumulative CFR levels respectively, while LRA and SVM predict suboptimal results. b.In OWID's data, CART and Bagging perform in a full accuracy for the cumulative label. The highest accuracies for the monthly and smoothed labels are evaluated from RFA and LRA. c.Combining oversampling with ensemble learning algorithms proved to be an effective strategy. However, it requires a large data size to get optimal performance. d.The high accuracy, F1–score and precision of the RFA and KNN models suggests that they have the capability of separately classifying weekly and cumulative COVID-19 fatality rates into low, moderate, or high.&nbsp;&nbsp;

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Machine Learning for Case Fatality Rate Classification

Project Background In the present era, public spaces play a major role in daily life especially in crowded areas like Hong Kong where maintaining public health through hygienic practices is vital. The various public digital terminals such as fast-food restaurant, ticket machines, and public transport navigation systems are used by thousands on a daily basis.&nbsp;These terminals are high-touch surfaces which means the risk of disease transmission is very high— more so with the current global health issues such as COVID-19always looming large.&nbsp;This project brings in a state-of-the-art touch-free user interaction system using gesture recognition technology to make these interfaces free from any physical contact requirements. This would greatly promote public health and safety by eliminating the need for people to touch these surfaces at all levels. The need for this technology is highlighted by the growing attention and alarm about cleanliness in communal spaces. Touchscreens, buttons— these relics from a bygone era can serve as breeding grounds for bacteria that might spiral into epidemics threatening public health all because of a simple touch. With this project aiming to introduce a contactless system, the endeavor hopes to present an innovative, secure, effective, and hygienic means where people can easily transact with digital terminal without any physical contact.&nbsp;Project Objectives • Innovate Interaction: The initiative aims to create a universally fitting, touchless interaction module which can easily be fitted into current systems and easily designed into new ones, thus promoting innovation in public health safety. • Enhance Public Health Safety: The core objective of this health concern is minimizing the public’s exposure to disease-causing pathogens due to shared surfaces. This module goes a long way in curbing the spread of both viral and bacterial infections through touch points within public spaces. • Boost Accessibility and Ease of Use: By creating an interface that operates through intuitive gestures, the module caters to a broad spectrum of the population. • Seamless Integration: In terms of integration, the design guarantees that the module can easily blend with different digital infrastructures. As a result, upgrades can be done without complications and many people can adopt it widely without interfering with how their current systems operate. Technologies Employed • ESP32 Microcontroller: This powerful microcontroller is at the heart of the module, handling all data processing and connectivity. Its robust performance ensures the system operates efficiently under various conditions. • PAJ7620 Gesture Recognition Sensor: Utilizing this advanced sensor, the module can detect a wide array of user gestures—such as swipes and selections—which are then translated into commands for the digital terminal. The sensor’s ability to accurately interpret gestures ensures a smooth user experience.• Customizable Software Interface: The software interface is designed to be adaptable, allowing users and administrators to personalize gesture-command mappings to fit different environmental and operational needs. This flexibility enhances the module’s utility across diverse settings. • Reliable Communication Protocols: A novel handshake protocol, inspired by proven HTTP communication techniques, is employed to ensure secure and stable connections between the touchfree module and host devices. This protocol guarantees that the interactions are not only seamless but also secure from interruptions.&nbsp;Detailed Functionality and Features • Advanced Gesture Recognition: The system allows users to interact with menus, make selections, and navigate through options using straightforward hand movements. Recognizable gestures include swiping in various directions and air tapping, facilitating an interaction that feels natural and is easy to adopt. • Handshake Algorithm: Essential for secure communication, the handshake algorithm operates through several stages: 1. Initialization: Automatically detects when the ESP32 module is connected to a host device. 2. Signal Exchange: Engages in a robust exchange of predefined signals to establish a secure connection. 3. Confirmation: Confirms a successful handshake before activating the module to receive and interpret gesture inputs. • Modular Design: The module’s design is inherently modular, allowing for easy installation on a wide range of terminal types—from small ticket machines to large informational kiosks—without the need for extensive modifications. • Customizable User Profiles: The system supports the creation of multiple user profiles, enabling individual users to save and switch between different configurations of gesture controls. This feature significantly enhances personalization and ease of use. • Reliable Case Manufacturing: The module is encased in a robust, custom-designed enclosure that includes an innovative angle adjustment mechanism. This feature allows the device to be optimally positioned based on the terminal’s installation height and angle, enhancing usability and user comfort across various public terminals. ConclusionIn conclusion, this project will change the way people interact with public digital systems in Hong Kong forever.&nbsp;Not only does it promote public health safety but it also aligns itself with the digital transformation goals of EDPS Fund. With support from the fund, the project will further refine its technology offerings— leading towards a more secure, accessible and hygienic public digital infrastructure. The launch of this touch-free module is a significant stride in ensuring safety and efficiency of public interactions— even as we face present and future health challenges.

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Touch-Free Interaction for Public Terminals

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This project aims to develop an integrated approach to control and predict blood glucose levels in patients with type 1 diabetes (T1D) by combining an insulin delivery method with advanced artificial intelligence (AI) algorithms. The project consists of two main parts: (1) the implementation of an insulin delivery system using a proportional-integral-derivative (PID) controller, and (2) the utilization of continuous glucose monitoring (CGM) data and AI algorithms for predicting future blood glucose levels, a capability beyond the scope of the PID controller. This combined system offers personalized treatment and with prediction capability it can give early warning to medical staff to manage T1D patients more effectively.&nbsp;&nbsp;Introduction:&nbsp;Type 1 diabetes (T1D) is a chronic condition characterized by insufficient insulin production, leading to unstable blood glucose levels. Maintaining optimal blood glucose control is crucial for T1D patients to prevent complications and ensure a&nbsp;healthy lifestyle. This project aims to address this challenge by integrating an insulin delivery method and AI-based glucose level prediction.&nbsp;&nbsp;Insulin Delivery System with PID Control:&nbsp;In the first part of the project, an innovative insulin delivery system is introduced. A virtual patient model is employed to simulate real patients’ blood glucose levels. The system utilizes a PID controller to regulate the amount of insulin delivered and achieve optimal blood glucose control. The PID controller is carefully tuned to prioritize the prevention of low blood glucose levels (hypoglycemia) while maintaining a balance to avoid high glucose levels (hyperglycemia), ensuring patient safety.&nbsp;The following graphs show the main concept and result of the first part of the project.&nbsp; <img src="https://strapi.edps.com.hk/uploads/sample1_e35ba963e3.jpg" alt="sample1.jpg"> AI Prediction Model with CGM Data:&nbsp;The second part of the project focuses on leveraging CGM data and advanced AI algorithms to predict patients’ future blood glucose levels. By using continuous and extensive data provided by CGM, a recurrent neural network (RNN) model is trained to offer alternative predictions and personalized treatment plans for T1D patients. To mitigate the impact of error accumulation over time, the AI model is designed to predict glucose levels within a specific near-future timeframe of approximately 5 to 15 minutes. This narrower prediction window enhances accuracy and reliability, enabling healthcare professionals to make timely interventions and adjustments.&nbsp;The following graphs show the main concept and result of the second part of the project.&nbsp; <img src="https://strapi.edps.com.hk/uploads/sample1_2_ca24cb4944.png" alt="sample1.2.png"> Advantages and Implications:&nbsp;The combination of the insulin delivery system with PID control and the AI prediction model provides several significant advantages. Firstly, it facilitates personalized treatment by considering individual variations in insulin requirements and metabolic responses. This approach ensures better glucose control to each patient’s specific needs. Secondly, the system offers early warning signs of high and low blood glucose episodes, enabling medical staff to intervene promptly and prevent potential complications.&nbsp;Conclusion:&nbsp;The Glucose Control Systems with PID Control and AI Prediction project presents an integrated approach to manage blood glucose levels in patients with T1D. By combining an insulin delivery system with PID control and AI prediction using CGM data, the project offers personalized treatment and early warning capabilities, ultimately contributing to improved glucose control and enhanced patient well-being.&nbsp;

Glucose Control Systems with PID Control and AI Prediction Abstract: 

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In the realm of art and design, the color of images holds great significance. Its purposefulness is multi-faceted, being paramount for attracting viewers and users in a variety of applications such as flat media, advertisements, and wallpapers. Often, the colors need to be deliberately changed to align with specific design themes or to achieve targeted visual goals. This project introduces a novel multi-modal image re-coloring pipeline, dynamically adjusting colors in images based on natural text, precise user clicks or reference images. By using Vision Transformers, semantic hints are effectively propagated across image and Sub-Pixel Convolution is utilized for real-time inference. The project aims to bring improvement in existing field of image re-colorization while giving end-users a semiautomated process of image re-colorization without specific needs for domain knowledge.&nbsp;&nbsp;Keywords — Interactive Colorization, Vision Transformer,&nbsp;Computational Photography, Image Manipulation, Neural&nbsp;Networks&nbsp;&nbsp;I.&nbsp;INTRODUCTION This project introduces a multi-modal interactive colorization pipeline with real time inference. Its key contributions are:&nbsp;&nbsp;<ul><li>Multi-modal (text, clicks and exemplar image) conditional image colorization using hint points as intermediate representation.&nbsp;</li><li>Vision Transformer with Sub-Pixel convolution achieving real time inference without requiring extensive computational resources.&nbsp;</li></ul>&nbsp;II.&nbsp;METHODOLOGY&nbsp;<ol><li>A.&nbsp;User Hint Generation&nbsp;</li></ol>In the first step, our model converts different types of user input to hint point masks which acts as an input alongside the image for colorization operation. The model supports three types of input. For textual input, we use&nbsp; a special pipeline utilizing CLIP [1] to convert&nbsp; to hint points. For reference image, we use warping and deep learning based [2] operation to generate hint points. Lastly, users can also manually select their preferred points and desired color through precise clicks and color selection through color gamut.&nbsp;<ol><li>B.&nbsp;Colorization Operation&nbsp;</li></ol>As seen in Figure 1, Once Hint points are generated, patches of input image are created which is encoded in a positional layer and passed into a Vision Transformer [3] backbone.&nbsp;This backbone effectively propagates the user hint points respecting semantic context. Once the features are generated the image is up sampled using a sub-pixel convolution&nbsp;technique, which reshapes channel dimension to increase&nbsp;spatial resolution. However, for larger ratio, artefacts can be produced. Because of this, an additional Stabilizing layer (which is a modified Convolutional Neural Network) isadded to group and scale neighboring pixels to achieve&nbsp;smoother upscaling. This allows our model to achieve real time inference without requiring extensive computational resources.&nbsp; &nbsp;<img src="https://strapi.edps.com.hk/uploads/sample2_c06a5160bd.jpg" alt="sample2_.jpg"> Fig. 1. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; The model takes as input an image and user hint (generated through text, reference image or precise clicks), and passes it into a vision transformer encoder and performs stabilized sub-pixel convolution to generate precise color with rich semantic understanding.&nbsp;&nbsp;III.&nbsp;METHODOLOGY&nbsp;TABLE&nbsp;I&nbsp;:&nbsp;INFERENCE SPEED OF CAPSTONE MODEL AGAINST OTHER BASE LINE MODELS. <img src="https://strapi.edps.com.hk/uploads/sample2_2_f862551426.png" alt="sample2.2.png"> Table 1 highlights, Capstone model’s efficient inference, particularly enabled by its sub-pixel convolution, which is significantly faster than traditional decoder based models.&nbsp; IV.&nbsp;CONCLUSION&nbsp; In conclusion, our interactive multi-modal image recolorization pipeline marks a good advancement in art and&nbsp;design applications, blending user input with cutting-edge AI to deliver real-time, semantically aware colorization.&nbsp; REFERENCES&nbsp;<ol><li>A. Radford et al., “Learning Transferable Visual Models From Natural Language Supervision,” CVPR, pp. 8748–8763, Jul. 2021.&nbsp;</li><li>B. &nbsp;&nbsp;&nbsp; Zhang et al.,&nbsp;“Deep Exemplar-Based Video Colorization,” Conference &nbsp;on Computer Vision and Pattern Recognition, Jun. 2019, doi: 10.1109/cvpr.2019.00824.&nbsp;</li><li>Z. Huang, N. Zhao, and J. Liao, “UniColor: A Unified Framework for&nbsp;</li></ol>Multi-Modal &nbsp;&nbsp;&nbsp;&nbsp; Colorization &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; with &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Transformer,” arXiv &nbsp;&nbsp; (Cornell University), Jan. 2022, doi: 10.48550/arxiv.2209.11223.&nbsp; <img src="https://strapi.edps.com.hk/uploads/sample2_3_741f93d728.jpg" alt="sample2.3.jpg"><img src="https://strapi.edps.com.hk/uploads/sample2_4_777e8efa5b.jpg" alt="sample2.4.jpg"> &nbsp;&nbsp;

Image Re-Colorization for Art & Design 

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Abstract:&nbsp;The project presents a high performance online flash order (Seckill) system – OrderFusion. It is built with intelligent design, queuing and caching techniques. The test result shows the solution of Queue + Cache outperformed the tradition MySQL solution, especially under high concurrency situation. The project is through 7 mouths, with the best final result of 77% decrease in delay of access, 3 times throughput and 99% decrease in denial of service incidents, comparing with tradition solution.&nbsp;Keywords:&nbsp;Seckill, Springboot, Redis, High-Performance, Online Order System&nbsp;&nbsp;<h3>I.INTRODUCTION</h3>With the popularity of online Seckill campaigns, order platforms are under tremendous operational pressure. The full solution, named with OrderFusion, is targeted to solve this problem.&nbsp;OrderFusion&nbsp;aims to work as a high performance/ concurrency online Seckill ordering system with Ultra high availability, which leverages strategies along with intelligent design, queuing, caching mechanisms and advanced security measures. Additionally, the system contains a lot of add on solution, like Chatbot powered by GPT4, automated workflows, blockchain logging function and single point authentication (SSO).&nbsp;The key challenges involve processing heavy traffic loads effectively, handling inventory discrepancies, resolving order processing issues during peak periods, and preventing performance declines ensuring security and reliability.&nbsp;In the evaluating with Apache JMeter, the result shows the solution of Queue + Cache outperformed the tradition&nbsp;MySQL solution, especially under high concurrency situation.&nbsp;&nbsp;<h3>II. DESIGN/METHODOLOGY/IMPLEMENTATION</h3>The core of OrderFusion revolves around Seckill Component, which represents high concurrency. This module follows on first-come-first-serve basis, where users join Seckill events and are put into waiting lists.&nbsp; The system uses RabbitMQ, an open-source message broker, to manage the high volume of Seckill requests by queuing them for asynchronous handling.&nbsp;Redis plays a role in caching data such, as Seckill outcomes and event specifics, which helps reduce server load and ensures rapid data retrieval. The Seckill process comprises user involvement tracking, inventory validation and updating, database transactions and user input. Notable features include inventory monitoring and locking mechanisms.&nbsp; <img src="https://strapi.edps.com.hk/uploads/SAMPLE_3_1_36b41f7d0f.png" alt="SAMPLE3.1.png"> Figure: Entire process of Seckill in OrderFusion<h3>&nbsp;</h3><h3>III.EVALAUATION AND RESULTS</h3>The report evaluates the performance of the OrderFusion system using traditional MySQL and Queue + Cache architectures. Apache JMeter was used for stress testing, providing various result views.&nbsp;Throughput, measured as requests processed per unit time, was tested under different concurrent client thread scenarios. The Queue + Cache approach consistently outperformed MySQL, especially when concurrent threads exceeded 500. However, throughput initially decreased with an increase in threads, then increased before decreasing again due to limitations in Redis Cache Pool, RabbitMQ Queue, and Docker container specifications. As concurrent threads increased beyond a certain point, the Queue + Cache's parallel processing capabilities led to a rise in throughput.&nbsp; <img src="https://strapi.edps.com.hk/uploads/SAMPLE_3_2_35ebf0e081.png" alt="SAMPLE3.2.png"> Figure: Throughput Testing Result&nbsp; The Queue + Cache approach also showed better service guarantees in high concurrency scenarios, making it suitable for high-performance online systems like OrderFusion. IV.CONCLUSION The OrderFusion project, a seven-month software engineering endeavor, successfully incorporated advanced technologies like Redis, JWT, and RabbitMQ queues, achieving enhanced performance and security. It significantly improved system efficiency with a 77% reduction in access latency, a threefold increase in throughput, and a 99% reduction in denial of service.&nbsp;This accomplishment illustrates the importance of strategic planning, innovation, and diligent execution in software development, providing insights for future projects.&nbsp;&nbsp;&nbsp;<h3>Highlight Preview&nbsp;</h3>The main task of this project is the development work. For the developers, it was&nbsp;<ul><li>A complete software engineering journey lasting 7 months.</li><li>A comprehensive experience utilizing the knowledge &amp; skills of PolyU undergraduate learning.</li></ul><img src="https://strapi.edps.com.hk/uploads/SAMPLE_3_3_ad8b5a0d3d.png" alt="SAMPLE3.3.png">Figure: OrderFusion Technology Ecology&nbsp; Named OrderFusion, the solution integrates cutting-edge technologies and patterns from the online ordering industry, such as Redis, JWT, RabbitMQ queues, and more, like Figure 2. The public service is available now, for public evaluation, at&nbsp;<a href="https://orderfusion.hirsun.tech/">https://orderfusion.hirsun.tech.</a>&nbsp; <img src="https://strapi.edps.com.hk/uploads/SAMPLE_3_4_050bd6ef23.png" alt="SAMPLE3.4.png"> Figure: OrderFusion UI Design&nbsp;

Technical Corner

Rise of Generative AI in Software Development and Support

Rise of Generative AI in Software Development and Support

Top 5 AI-Driven Innovations in IT for 2025

Virtual Avatar Modelling & Animation in AR

Machine Learning for Case Fatality Rate Classification

Touch-Free Interaction for Public Terminals

Glucose Control Systems with PID Control and AI Prediction Abstract:

Image Re-Colorization for Art & Design

SecKill: High-Performance Online Flash Order System