Of the hundreds of activities, which one is your favorite There is a wide variety of activities every day in Taiwan, including forums, exhibitions, lectures, and free experiences Event organizers need to use their own media event official website, Facebook, Instagram, event websites, and pay media for marketing, but often do not know where the target customer is, and cannot accurately estimate the number of attendees Records of people's participation in various activities are used through this "Automated Exhibition Matching System," and the data is analyzed to predict what type of activities users like It automatically matches activities with users to provide fast, easy, and accurate marketing and promotion methods There are many types of activity themes, marketing and advertising costs are high, but results are poor A domestic curation company is working with township offices and tourism service providers in the marketing of rural villages It organizes a variety of activities every year, such as agricultural product exhibitions, rural experiences, parent-child themed experience days, and agricultural specialty product marketing Due to the vastly different characteristics of participants in the activities, which have different themes, effective and accurate market cannot be carried out when promoting the activities, which can easily lead to a significant increase in marketing expenses and low matching rate For example, when the curator organizes an exhibition with 200 booths in the venue, the overall marketing cost is about NT800,000 to NT12 million, in which construction of the official website, marketing on the event website, and text message notifications account for about NT400,000 to NT600,000, but the event's matching rate is less than 20, and precision marketing aimed at the target customer group cannot be carried out After adopting the "Automated Exhibition Matching System," it can automatically select suitable customer groups for push notifications Depending on the scale of the event and the exhibition period, the system rental fee is only about NT200,000 to NT300,000, significantly reducing event promotion costs Precision smart marketing, distributing coupons to target groups The "Automated Exhibition Matching System" currently has an accuracy of 82 and can effectively screen target consumers In terms of module accuracy, large amounts of data and data that no longer contains noise will be used to further improve the accuracy in the future Coupons will be distributed to target groups, so that groups that receive the coupons will actually participate in the event After adding value through AI, the system can replace the manual random distribution of coupons without a specific target The AI module can automatically adjust the weights to more accurately lock on to the target group The weighted formula uses the CRM system of the curation company to output analysis of member behavior in the past In the second stage, the AI weighted formula will be used to find the best calculation formula for different activity categories through automatic correction Service Framework of the Automated Exhibition Matching System During the implementation period, Fengchun Technology was troubled by the problem of "the AI classifier module training and learning not finding the best solution" After discussions with AI engineers, the company found that a shortcoming of "back-propagation neural network" is that it only finds the "local" best solution rather than the "global" best result during learning The goal of improving accuracy can be achieved by increasing the number of training times and adjusting parameters Expand system functions, connect member databases, and conduct behavioral analysis The "Exhibition Event Matching System" mainly provides event organizers, independent curators and the public with event matching Next, the platform functions will be expanded for use by event participants, and the accuracy of the AI classifier will be improved In the future, an ocean culture exhibition will be organized with the ocean industry, and use this system to find ocean culture promoters, connecting and expanding member databases for behavioral analysis

A brilliant idea transforming AI facial recognition technology As artificial intelligence develops, more and more industries are embracing AI technology, even subtly entering into people's lives As most bakeries sell freshly made bread and pastries, which typically do not have barcodes, they rely on cashiers to visually identify each item and enter the type and price of the bread Thus, inspired by AI facial recognition technology, if such artificial intelligence could identify hundreds of types of bread, it could enhance checkout efficiency Diverse handmade breads delight customers but challenge clerks A local bakery has over 100 types of bread, regularly updating or adding new products, offering customers a variety of choices this poses a challenge for cashiers It takes two months to train a cashier, but even after they start, there's still a 5 to 10 error rate due to bread recognition mistakes each month, especially during peak checkout times after work, causing bottlenecks and further errors due to the stress on cashiers The difficulty in training cashiers and the lack of precision in the checkout process have long troubled businesses When baking meets artificial intelligence, it sparks a marvelous retail experience In typical bakeries, bread is sold 'naked' immediately after baking and then 'packaged' when it cools to room temperature Both methods require cashiers to recognize and remember the prices and undergo two months of training before they can work the cash register Even then, there is still a 5 to 10 error rate each month My Dee Bakery, with its extensive range of over 100 bread types, poses a significant challenge for cashiers Due to Yun Kui Technology Co, Ltd's expertise in developing iPad POS systems, which are designed to be simple, convenient, and easy to use, they allow businesses to check out efficiently and accurately Therefore, integrating the existing POS system with AI image recognition capabilities enables businesses to carry out transactions more efficiently and precisely AI bread recognition model operational schematic Image provided by Yun Kui Technology The execution can be simplified into eight steps, which include 1 Data collection Take bread image data at bakeries 2 Image annotation The image data is handed over to Mu Kesi Co, Ltd for manual annotation 3 AI modeling and training Managed by Mu Kesi, who adjusts AI models and training 4 iPad POS adjustment Simultaneous adjustments of the UI interface on the POS side and backend integration with the AI model 5 Start testing Once Mu Kesi reaches over 95 recognition accuracy with current data, formal integration testing begins 6 Real scene testing Move to the bakery to gather data and verify the correct recognition rates 7 Planning real scene application accessories When recognition accuracy exceeds 98, design accessories for on-site checkout, such as remote cameras and projection light sources 8 Official Application Integration with electronic receipts goes live POS machine AI bread recognition checkout process Start recognition - Recognition complete - Checkout - Confirm checkout, takes only 3 seconds Image provided by Yun Kui Technology AI bread recognition system, making multitasking easy After adding AI capabilities, not only can it save upfront training time and costs for bakery cashiers and reduce costs from recognition errors, but it can also speed up the checkout process and efficiency, increasing customer satisfaction This can later be promoted to various retail industries, expanding the new map of smart retail Before and after comparison chart of the bread checkout process with AI valuation Image provided by Yun Kui Technology「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Best practices in AIIoT implementation As our country enters the first year of 5G commercialization this year, the integration of the Internet of Things with artificial intelligence to transmit data with zero delay will enable everyone to effectively manage all data Toilet 'odor monitoring' has become the best platform A certain chain supermarket in the country has 47 stores nationwide and in recent years, as competition in the supermarket industry intensifies, some stores plan seating areas and toilets for customer use Currently, the average number of customer complaints about toilet cleanliness in a certain store of the chain supermarket is about 10 times per month, which is notably higher than other stores, thus they hope to solve the problem of high complaint rates through artificial intelligence Customers frequently complain about dirty toilets The toilets of a certain store of the chain supermarket are inspected at 12 PM and 6 PM daily, and cleaned during the night shift Customer service staff often receive complaints about the dirty and smelly toilet environment, causing the need to constantly deploy manpower for toilet maintenance To achieve a 100 odor-free toilet, it would be necessary to employ a cleaning staff member permanently present in the toilet, however, this solution is too costly and wastes manpower Through a collaboration between Guo Xing Information Co, Ltd and the chain supermarket, the National Taichung University of Science and Technology AI team was commissioned to address this vexing issue using IoT and AI technologies IoT Monitoring x AI Manpower Dispatch Guo Xing Information equipped the toilet door locks with IoT sensory devices, and installed 'odor sensors' and 'air temperature and humidity sensors' outside the cubicles By monitoring the behavior, frequency, and timing of door usage, it predicts the cleanliness of the cubicle If a person opens the toilet door and closes it quickly, and if more than three consecutive people exhibit the same behavior, it predicts that the cubicle is dirty enough to require cleaning In terms of manpower dispatch, the system predicts staffing needs based on user frequency, holidays, and festive events, dynamically adjusts manpower reserves, and calculates the minimum staffing needed to maintain toilet comfort Service architecture of the Smart Toilet Quality Monitoring Platform This 'Smart Toilet Quality Monitoring Platform' is installed in the open-area toilets of business premises, collecting data such as usage frequency, time, odor intensity, air temperature, and humidity, and transmitting it to the platform for AI data analysis This enables management to understand the real-time usage, frequency, and dirtiness of the toilets, providing alerts to dispatch cleaning staff and take responsive actions It also assists managers in environmental quality monitoring and dirty conditions predictive dispatching Through historical data analysis, it suggests dynamic manpower deployment during different time intervals for effective human resource management and utilization Smart toilet detection, reducing cleaning labor costs After field tests of the Enhanced AI Smart Toilet Monitoring Platform, the retailer found the real-time monitoring and alert features extremely practical and is willing to continue using them Regarding 'reducing the number of complaints', a one-month data validation showed a significant effect, while 'dynamic manpower dispatch' is still under evaluation and validation After a month of data evaluation, a noticeable improvement was found in 'monitoring toilet usage' and 'reducing complaints' After trial use by the retailer, they are also willing to continue using the system In the future, notifications will be made according to 'usage time' to prevent accidents within the cubicles There will also be future deployments and promotions priced at low, mid, and high levels「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Combatting the epidemic is like fighting a fire With the increasing influx of returning residents, the pressure of Wuhan virus quarantine is mounting, consuming more time Shortening quarantine times will positively benefit disease prevention efforts At a hospital in Southern Taiwan, the 'Smart Medical Clinical Decision Support System' has helped reduce the time taken for high-risk patients from entry at the quarantine station to clinical decisions by doctors It originally took about 2 and a half hours, but now it's down to less than 30 minutes, increasing the quarantine efficiency by 5 to 6 times and significantly reducing the risk of cross-infection between medical personnel and patients, as well as the manpower needed for quarantine As waves of overseas students densely return to Taiwan, not only the Central Epidemic Command Center, but also various medical institutions are tightening up, closely monitoring every quarantined individual There are also concerns about the potential infection risks to colleagues, which is exhausting At this point, employing AI technology to enhance quarantine efficiency is indeed a great blessing for the medical units and the health of the nation AI-Assisted Medicine Multiplies, Becoming a Hero in Pandemic Control To combat the severe pandemic of novel coronavirus, the hospital has integrated various smart medical techs and developed the 'Smart Medical Clinical Decision Support System', raising quarantine efficiency by 5 to 6 times It has shortened the time taken for high-risk patients from entering the quarantine station to doctors making clinical decisions from 2 and a half hours to less than 30 minutes, effectively reducing the risk of cross-infections The 'Smart Medical Clinical Decision Support System' implemented by the hospital includes three components front-end automation of medical records, AI-assisted interpretation of chest X-rays for diagnosing pneumonia, and continuous updates of clinical decisions based on the latest epidemic data provided by the health department This significantly enhances the hospital's response and decision-making ability in quarantine and epidemic prevention, and greatly benefits Taiwan's anti-epidemic efforts through the multiplicative effect of AI-assisted medicine National Cheng Kung University collaborates with the hospital, using smart medical technology to enhance quarantine efficiency Photo source Official website In the aspect of medical record automation, existing medical institutions often use traditional paper or verbal reporting, which potentially increases the risk of contact infections among medical staff and patients The automated medical records system in this hospital allows patients to fill out their own medical history, including travel, occupation, contacts, and clustering, using tablet computers These records are uploaded to the electronic medical record system, enabling immediate access by medical staff to make clinical decisions Each tablet is disinfected with alcohol after every use, reducing the risk of cross-infection and enhancing the efficiency of the quarantine station The hospital's Wuhan pneumonia screening shows a sensitivity and accuracy of up to 80 and 90, respectively The 'Chest X-Ray AI Interpretation for Pneumonia System Model' developed by the hospital's Department of Radiology, with active participation from Professor Yong-Nian Sun's team at the College of Electrical Engineering and Computer Science Utilizing a tuberculosis X-ray AI auto-interpretation model developed by a previous AI biotech medical innovation research center project, it was adapted to the hospitals' pneumonia imaging data The collaboration between the parties ensured rapid completion Currently assisting in over 152 suspected Wuhan pneumonia screenings, sensitivities and accuracies of up to 80 and 90 have been achieved, respectively Moreover, for students conducting in-home quarantine at school dormitories, the university has adopted a smart monitoring approach with a 'Warm Heart Smart Bracelet' developed by a cross-disciplinary team, which continuously monitors quarantined individuals' body temperatures and heart rates as indicators for predicting symptoms When a rise in body temperature is detected, individuals can proactively confirm abnormal symptoms via a smartphone app and be prompted to seek medical attention Currently, bracelets are collected weekly and data is centrally uploaded to a cloud platform by the management staff for ongoing tracking, wholly enhancing the level of pandemic control internally and externally The university's cross-disciplinary team uses 'Warm Heart Smart Bracelets' to implement home quarantine policies effectively Photo source Official website「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Fast fashion in clothing, small quantities, diverse styles, short delivery times The textile industry faces the impact of the fast fashion trend among clothing brands, affecting the entire supply chain Global brand channels are promoting zero inventory, short delivery periods, and small-scale customization Balancing production time, quality, and cost is challenging Often, there is a discrepancy between ODM predictions and actual demands from brand owners, causing issues in material management and excessive inventory costs Due to inaccurate demand forecasts from customers, it often leads to difficulties in material preparation Excessive materials can increase leftover stock, while insufficient materials may delay delivery This project aims to establish an AI-based material demand forecast model specifically for major domestic manufacturers AI calculates sales trends to further predict demand The advisory team collaborates with Shentong Information Technology to mainly use the LSTM algorithm for the AI foundation The goal is to predict the next sales cycle based on past sales records, utilizing simple regression to complex 'Time Series Analysis' in statistics Usually, a period's sales volume closely relates to the previous period's, unless there is a major event, in which case it would typically follow a pattern There are various patterns of sales volume forecasts, including revenue, profit, customer counts, park visits, sales numberamount, etc This will take the example of a factory's monthly shipment batches, using the LSTM model to predict the next month's shipment batches Material Demand Analysis Execution Framework This project plans to establish a customer-specific material demand AI prediction model During the planning phase, three different machine learning algorithms were used to prototype the AI model Logistic Regression Algorithm Gradient Boosting Algorithm Deep Learning Algorithm Material Demand AI Prediction Model Planning Demand forecast error reduced from a maximum of 70 to 35, significantly reducing inventory volumes This project estimates customer demands, required material types, supply sources, and customer delivery dates using machine learning to establish a primary material procurement prediction system It reduces the prediction error of demand from the top five international customers from a high of 70 to 35, significantly lessening the amount of inventory needed「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Labor-intensive, prone to oversights and errors, low shipping efficiency A domestic hook-and-loop tape traditional manufacturing transformation and brand management company has expanded new markets and business opportunities through the e-commerce platform model This requires reliance on substantial labor for product listing, order organizing, inventory management, and shipment tracking This results in limited product varieties and quantities that can be handled, and manual data entry is often prone to oversights or errors, affecting shipping efficiency and customer satisfaction, which is critical for the competitive advantage of the business in e-commerce Internally, many operations rely heavily on repetitive tasks across various computer systems, web pages, emails, etc Currently listed on 15 e-commerce platforms, updating single e-commerce information alone requires 2-3 months over 200 items, making rapid expansion difficult limited by manpower, product information is not detailed enough, leading to doubts in e-commerce reviews, affecting orders and subsequent satisfaction Presently, orders are only confirmed once a day, leading to an information gap of up to 24 hours Annually, there are over ten thousand orders to process into shipment orders, typically accumulating for 15-30 days before once grouping deductions from inventory, resulting in always inaccurate stock levels Streamlined Client Interface, Accelerating Implementation Efficiency The mentoring team collaborates with Ruijing Engineering Technology to integrate AI and RPA technologies through a web-based architecture Robotic Process Automation RPA applications are not installed on the local desktop but are stored on a server and accessed only when needed by the user This technology, known as Thin Client, provides higher performance and security compared to the Thick Client, which requires downloading applications and data to the local desktop The Thin Client does not require downloads on the local machine RPA collaborative service features include Web Scraping Complex web data collection and arrangement Email manipulation Data analysis and disassembly of content and attachments Web operation Precise and rapid web operations or filling in specific fields Application operation Timed positioning operations of other window applications Data processing Data format conversion, decomposition, and reassembly File Exchange Management Timed file production, adddeletemodify, FTP uploaddownload Database operation Heterogeneous database data exchange, read or write to a specific DB Data recognition Fixed format field data processing screenshot, snapshot, alphanumeric text parsing and recognition Scheduling Can be timed, repeated, cross-process all the above processes Alert mechanism Email, Line Notification etc designated or broadcast notification Software Robot Technology Solution Execution Architecture AI software robots enhance the processing speed of orders, inventory management, and purchasing in manufacturing operations, developing automated services to avoid data duplication and input errors, and seamlessly integrating processes across systems operating 247 The war room panel facilitates statistical analysis and real-time sales conditions on each e-commerce platform, predicting and optimizing product inventory Direct Purchase Order Process Automation Robot E-commerce Information War Room Statistical Analysis Dashboard Software Zero Errors, Reducing Costs by 15 to 90 面對快速變化又競爭激烈的市場環境，更需要減少重複性、低產值的工作，將人力運用在更高價值的工作上。 Facing a rapidly changing and highly competitive market environment, it is essential to reduce repetitive, low-value tasks, focusing manpower on higher-value work RPA software robots are 15 times more efficient than indirect staff, also enhancing process quality to near-zero error rate execution quality, offering opportunities to reduce costs by 15 to 90 Since it doesn't require significant changes to existing workflows, businesses generally do not need to spend substantial manpower on retraining or adapting to new workflows, which contributes to a higher acceptance rate among businesses Even in software deployment, it only takes about 4-5 weeks to go live「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

Miniaturization of products, client demands full inspection A listed electronic component manufacturer in Taichung, responding to the 5G era injecting new growth momentum into the quartz component industry, especially under the explosion of 5G opportunities, the importance of quartz components will play a more crucial role than in the past in consumer products As frequency components move towards miniaturization and at the same time demand high precision, the manufacturing processes are more susceptible to subtle factors, necessitating manufacturers to manage comprehensive data across all aspects including human, machine, material, method, and environment to quickly identify key defective factors in complex production environments Differing perceptions of defects, difficulty in enhancing quality consistency With the trend of miniaturization and complexity of electronic components, visual inspection on the production line has four main functions including measurement, identification, positioning, and inspection, with inspection being the most challenging part as most electronics manufacturers still rely on traditional manual visual inspection Taking the PCB industry, where Automated Optical Inspection AOI technology has the highest penetration rate, as an example, a research institution once investigated and found that when two individuals inspect the same PCBA board four times, their mutual agreement rate was less than 28, and the self-agreement rate was only about 44 Due to differing perceptions of defects among on-site personnel, even automated machine vision can still lead to inconsistencies in product quality due to system settings or differences among quality control staff 偲捷科技檢測AI化，降低過篩率2030 With the support of the advisory team, collaboration with Sijie Technology aimed at the defects in the sealing process Based on CNN Convolutional Neural Network, the integration of multiple models introduced an AI recognition module to aid in the optimization of subsequent AOI tests, aiming to improve the accuracy of inspection equipment It is estimated that after introducing AI visual recognition, the over-screening rate could be effectively reduced to 2030 Thus, the industry, needing smarter inspection systems, has started applying AI technology to assist AOI equipment in optimizing subsequent screening tests AI-powered AOI Inspection Solution Cross-Model Design Concept Sealing AOI Inspection Trial Results Reducing false rejects, cutting manual screening workload by 50 The project, through a deep learning network architecture, reclassifies defects detected including true and false defects, and further classifies them to reduce the false reject rate of the traditional AOI solution This is anticipated to further aid manual inspectors in reducing more than 50 of the inspection screening volume, addressing current production line issues of relying heavily on manual re-inspection and low efficiency Future goals include integrating robotic arms for automatic loading and unloading, and analyzing defect causes to optimize production process parameters「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」

AI is thriving in healthcare services, where pharmacists in community pharmacies are essential for providing drug knowledge and pharmaceutical services However, these pharmacists often spend much time manually processing prescription entries into systems, which takes away from the time they could spend on drug education, medication effectiveness tracking, and other professional services How can AI help community pharmacies to support pharmacists Tedious, time-consuming, and repetitive tasks, and AI solutions Pharmacy operations are under threat from new market dynamics and limited profit-making modes, making digital upgrades challenging for single-pharmacist community pharmacies Pharmacists, taking on multiple roles to understand the health levels of community residents, face tedious, time-consuming, and highly repetitive tasks that hinder the quality of service and make it difficult to respond to customers non-stop all year round Smart Pharmacist Assistant Service Platform Enabled by Jiankangli Technology's smart pharmacist assistant service platform's system architecture, paired with the mobile application 'Smart Good Doctor' and the backend system 'Smart Good Pharmacist', along with the integration of external development feature resources 'OCR Prescription Recognition' and 'RPA Process Automation Training Module RPA library' Primarily applied in clinics and pharmacies at the primary healthcare level, this aims to solve various operational challenges and pain points It includes using digital technology to improve work efficiency, bridging the gap between the public and medical institutions, enhancing the medical relationship, achieving better operational and manpower benefits Additionally, it enhances medication safety for the public and improves their knowledge on medications, while also reducing the daily burden on pharmacists in pharmaceutical services Smart Pharmacist Assistant Project In the current stage, the Institute for Information Industry's team is guiding the integration of pharmacy information system vendors with AI startups, focusing on the development of intelligent prescription image recognition technologies, along with drug image recognition and smart drug scheduling reminder technologies as key research areas This has led to the implementation of practical deployments in 12 community pharmacies in Greater Taipei With the help of the Taiwan Young Pharmacists Association in promoting these technologies, over 100 community pharmacy proprietors have expressed interest in adopting such technologies Once the integration of these service platform systems is complete, it will become a model for promoting AI services in Taiwan's community pharmacy pharmaceutical services「Translated content is generated by ChatGPT and is for reference only Translation date：2024-05-19」