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      AI-based: AI-based attendance systems use facial recognition technology to automatically
                            mark  attendance  when students  enter  the  classroom.  The system  learns faces  and
                            maintains accurate records.

                       Security System
                          Time-based (Automation): Security cameras record continuously but do not analyse the
                            footage. Security personnel must manually check recordings.

                          AI-based: AI-based security systems detect unusual activities, recognise faces and send alerts
                            automatically. The system can learn patterns and improve detection accuracy over time.

                       Smart Fan System
                          Time-based (Automation): Fans run at fixed speed settings selected manually.

                          AI-based: AI-based fans detect room temperature and occupancy. The fan automatically
                            adjusts speed and turns OFF when no one is present.

                       Irrigation System:
                          Time-based (Automation): In this irrigation system, a timer waters the plants every day at

                            7 AM, regardless of weather conditions or soil moisture levels.
                          AI-based: An AI-based irrigation system waters plants based on soil moisture, weather
                            conditions and predictive analytics. The system analyses data and decides the best time

                            and amount of water required for plants.

                     Data Collection and Preparation

                     After defining the problem that requires AI, Data collection and preparation are the next important
                     phase of an AI project lifecycle. Data collection is a structured and systematic process of gathering
                     information  from various sources  such as observations,  measurements,  surveys and existing
                     records. This stage is an essential part of any AI project because the performance of an AI model
                     largely depends on the quality and quantity of data used. Accurate and relevant data helps in
                     building reliable AI systems, while poor-quality data can lead to incorrect predictions.
                     During this phase, data requirements are carefully reviewed based on the problem being solved.

                     The type  of data,  amount  of data  and  sources  of  data  are  identified.  If the  available  data  is
                     insufficient, additional data is collected to meet project objectives.
                     There are mainly three types of data sources:

                       Primary data source: Primary data is collected directly from the original source for a specific
                        purpose. This type of data is fresh, raw and highly accurate because it has not been processed
                        or modified by anyone else. Primary data is usually collected when specific information is
                        required that is not available from existing sources.
                        Common methods of primary data collection include:

                          Surveys  and  interviews: Surveys and interviews  are widely  used  to  collect  opinions,
                            feedback and preferences directly from individuals. Surveys may be conducted online,
                            offline or through phone calls.

                                                                                               AI Project Lifecycle    15

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