Next Idea vs Neurex

Introducing Next Idea - a new free to-do list app for staying organized and boosting your productivity Take control of your projects and tasks using a straightforward and proven methodology Having a trusted system to write down all your tasks and projects is the best way to get a clear mind, and help you generate new creative and productive ideas Key features: • Create projects for the tasks that require more than one physical action to complete • Easily move tasks to the top or to the bottom, to decide what to work on next • Add tags to your tasks based on the context required to complete them • Use the guided review feature to stay on top of your projects and tasks You can also: • Create recurring tasks and reminders • Sort your tasks from within any view, so that you can easily prioritize and decide what to do next • Move tasks to Someday if you don't plan on working on them soon, but still want to keep track of them • Defer tasks that you don't want to focus on until a later date • Sync your tasks securely across all your Apple devices using iCloud

Neurex is an expert system based on a multi-layered neural network. The era of neural networks and connectionism offers a new perspective on obtaining reliable knowledge for decision support and its user-friendly application. Traditional expert systems, which are rule-based and/or frame-based, often face challenges in creating a reliable knowledge base. Neural networks can overcome these difficulties. It's possible to create a knowledge base without experts, solely using data collections that describe the solved area, or with experts whose knowledge can be verified during the learning process. The expert system's usage process can be outlined as follows: 1. Definition of the Neural Network Topology: This step involves defining the number of input and output facts, as well as determining the number of hidden layers. 2. Formulation of Input and Output Facts (Attributes): Each fact is linked to a neuron in the input or output layer. The range of values for each attribute is also defined. 3. Definition of the Training Set: Patterns are entered using truth values (e.g., 0-100%) or values from the range defined in the previous steps. In case that patterns represent time series it is possible to define number of patterns in sequence (time window) for the prediction of the output. Value 1 says that there is no time context and patterns are time independent. 4. Learning Phase of the Network: The weights of the connections (synapses) between neurons, the slopes of the sigmoid functions, and the thresholds of the neurons are computed using the Back Propagation (BP) method. Options are available to define parameters for this process, such as the learning rate and the number of learning cycles. These values form the expert system's memory or knowledge base. The learning process's results are displayed using the mean squared error, and the index of the worst pattern and its percentage error is also shown. 5. Consultation with the System: In this phase, the values of the input facts are defined, after which the values of the output facts are immediately deduced.