Case 1: Smart Attendance system

Organizations are looking for a smart attendance system that utilizes new technologies to clock in & out from anywhere. With COVID-19 pandemic the usage of biometrics devices was ceased, and employees were asked to use other ways to record attendance such as card and manual signing document. However, these approaches are subject to breaches and misuse to ensure the right employee is at the right place to work. Organizations are looking for other possible approaches that utilize different media to record employee in/out. The approach must be secure and controlled so less misuse or breaches can occur, (2) it must be safe and not exposing people health and safety (3) easy for enrollment so it does not require collecting more data about employee (4) cost-effective and does not require investment or production of access media for all individuals such as RFID cards, so no need to worry about fake or buddy clocking.


Case 2: Intelligent Minutes of Meetings

With COVID-19 pandemic spreading, many traditional meeting transfers into virtual/online meeting. The frequency of meetings has increased dramatically and required a smart tool for managing these meetings. Organizations are seeking for a tool that has (1) note-taking capabilities where it listens and records notes using NLP natural language processing techniques (2) voice recognition so it identifies people while talking (3) NLP must be able to identify actions and build an action plan with an assignment to the right people (4) smart tool then will draft a minute of meetings where it records attendance, absence, actions to be taken, and circulate them to all invitees, (5) an added value feature will be enabling the tool to track and follow up actions with people, and update the action plan accordingly, plan the agenda for next meetings, and schedule the time and date by accessing invitee timetable to find out the most appropriate time.


Case 3: Addressing customers concerns in real-time

Today it has become common practice for people to be it Twitter, Instagram, Facebook or personal blogs, to share their day-to-day experiences. Traffic jams, bad service at the passport office, slow-to-respond government websites, however mundane it might seem, people feel compelled to share this information with the world. But where many see this content as being banal, it can be priceless to a well-organized government, looking to improve public services. Organizations are looking for semantic analytics capabilities to cluster data by the underlying semantic meaning. Data that was about traffic jams could all be grouped automatically. Data about long waits at an organization department or a poor customer
service experience could also be grouped, providing business decision-makers with a very precise picture of which areas require focus. Priorities can be set more quickly, resources can be allocated more efficiently, and better solutions can be delivered more rapidly. With historical data and semantic clustering, the final stage in the organization analytics is predictive analytics. By extracting several features from the historical data as well as the semantic and sentiment analysis, it might be possible to create predictive models to anticipate bottlenecks in various services. If organizations could predict when a particular service might be overwhelmed by demand and deploy sufficient resources in anticipation of such an event, then the organization’s customers might not experience any downtime of services and maintain a more positive view of their organizations.


Case 4: Waste Management

Waste management in the modern world has appeared as a serious issue. Waste management is a daily task in urban areas, which requires a large number of labour resources and affects natural, budgetary, efficiency, and social aspects. Many approaches have been proposed to optimize waste management, but the results are still too vague and cannot be applied in real systems, such as in universities or cities. Recently, there has been a trend of combining optimal waste management strategies with low-cost IoT architectures. Organizations are looking for a novel method that vigorously and efficiently achieves waste management by predicting the probability of the waste level in trash bins. By using machine learning and IoT, the system can optimize the collection of waste with the shortest path. The system must save time by finding the best route in the management of waste collection and avoid the empty trash bins and build the right route, and an optimized schedule for collection to save time, cost and effort.


Case 5: Build Smart, Safe & healthy Countries

Governments have seen a rapid surge of urbanization in the past two decades. This rapid increase in the urbanization has posed the need to augment and/or optimize the utilization of infrastructure and public sector administration to mitigate the challenges related to congestion, pollution, safety, living standards etc. Governments are in pursuit of exploring potential initiatives that contribute to the development of their country of future-making life safer and healthy for its citizens, residents and tourists. Towards this objective, participants of the hackfest are invited to build a working model through the application of AI technologies in the following suggested areas:

• Participation in decision making
• Enable transparent Governance and improve public finance management
• Build Innovative spirit & culture of entrepreneurship
• Facilitate trade and commerce
• Crowd management
• Smart parks and public facilities
• Detect fraud and corruption
• Improve crime reporting
• Reduce pollution and facilitate environmental protection
• Soil health monitoring & restoration
• Enable sustainable resource management
• Facilitate cultural facilities
• Improve health care facilities and health conditions of constituents
• Improve Housing quality
• Enhance education facilities and quality of education
• Attract tourists and augment tourism
• Enable sustainable, innovative & safe transport systems
• Assist vulnerable citizens

To view the resources of each case study, please refer to the document below