Neurex

Developer: Ivo Vondrak Apps

Category: Productivity

3K installs

ratings

+2.8K weekly installs

+3K monthly installs

ASO Keyword Dashboard

Tracking 93 keywords for Neurex in Google Play

Developer: Ivo Vondrak Apps Category: productivity

Neurex tracks 93 keywords (no keywords rank yet; 93 need traction). Key metrics: opportunity 69.8, difficulty 46.5.

Artificial neural network in your pocket

Tracked keywords

0 ranked • 93 not ranking yet

Top 10 coverage

—

Best rank — • Latest leader —

Avg opportunity

69.8

Top keyword: often

Avg difficulty

46.5

Lower scores indicate easier wins

Opportunity leaders

often

Opportunity: 73.0 • Difficulty: 49.9 • Rank —

Competitors: 755
68.1
input

Opportunity: 73.0 • Difficulty: 52.7 • Rank —

Competitors: 830
68.3
immediately

Opportunity: 72.0 • Difficulty: 45.3 • Rank —

Competitors: 1,118
69.6
traditional

Opportunity: 72.0 • Difficulty: 47.2 • Rank —

Competitors: 1,414
70.9
error

Opportunity: 72.0 • Difficulty: 42.4 • Rank —

Competitors: 372
62.4

Unranked opportunities

often

Opportunity: 73.0 • Difficulty: 49.9 • Competitors: 755
input

Opportunity: 73.0 • Difficulty: 52.7 • Competitors: 830
immediately

Opportunity: 72.0 • Difficulty: 45.3 • Competitors: 1,118
traditional

Opportunity: 72.0 • Difficulty: 47.2 • Competitors: 1,414
error

Opportunity: 72.0 • Difficulty: 42.4 • Competitors: 372

High competition keywords

new

Total apps: 193,474 • Major competitors: 25,823

Latest rank: — • Difficulty: 65.7
using

Total apps: 114,758 • Major competitors: 13,323

Latest rank: — • Difficulty: 64.0
without

Total apps: 113,596 • Major competitors: 12,654

Latest rank: — • Difficulty: 63.1
create

Total apps: 93,845 • Major competitors: 13,574

Latest rank: — • Difficulty: 69.1
available

Total apps: 92,463 • Major competitors: 9,952

Latest rank: — • Difficulty: 66.4

All tracked keywords

Includes opportunity, difficulty, rankings and competitor benchmarks

							Major Competitors
new	64	100	66	90 193,474 competing apps Median installs: 42,470 Avg rating: 3.0	—	—	25,823 major competitor apps
step	69	100	55	77 31,345 competing apps Median installs: 35,708 Avg rating: 2.9	—	—	3,571 major competitor apps
support	67	100	60	83 68,575 competing apps Median installs: 34,249 Avg rating: 3.0	—	—	7,371 major competitor apps
range	69	100	53	78 37,225 competing apps Median installs: 32,979 Avg rating: 2.8	—	—	4,354 major competitor apps
reliable	71	100	51	73 19,185 competing apps Median installs: 30,368 Avg rating: 2.7	—	—	1,887 major competitor apps
immediately	72	100	45	70 11,872 competing apps Median installs: 32,552 Avg rating: 2.5	—	—	1,118 major competitor apps
using	65	100	64	86 114,758 competing apps Median installs: 37,480 Avg rating: 2.8	—	—	13,323 major competitor apps
available	66	100	66	85 92,463 competing apps Median installs: 34,120 Avg rating: 2.9	—	—	9,952 major competitor apps
traditional	72	100	47	71 14,045 competing apps Median installs: 28,059 Avg rating: 2.8	—	—	1,414 major competitor apps
rate	70	100	50	75 24,148 competing apps Median installs: 33,875 Avg rating: 2.7	—	—	2,128 major competitor apps
process	70	100	50	74 20,843 competing apps Median installs: 26,049 Avg rating: 2.6	—	—	1,825 major competitor apps
error	72	100	42	62 4,512 competing apps Median installs: 31,886 Avg rating: 3.3	—	—	372 major competitor apps
create	66	100	69	85 93,845 competing apps Median installs: 45,191 Avg rating: 3.0	—	—	13,574 major competitor apps
face	70	100	55	75 24,650 competing apps Median installs: 46,450 Avg rating: 3.1	—	—	3,493 major competitor apps
without	65	100	63	86 113,596 competing apps Median installs: 37,274 Avg rating: 2.8	—	—	12,654 major competitor apps
knowledge	70	100	47	75 23,013 competing apps Median installs: 22,252 Avg rating: 2.6	—	—	1,315 major competitor apps
output	71	100	39	59 2,809 competing apps Median installs: 29,798 Avg rating: 2.7	—	—	255 major competitor apps
network	70	100	56	75 24,379 competing apps Median installs: 36,741 Avg rating: 2.9	—	—	2,898 major competitor apps
back	70	100	62	76 28,322 competing apps Median installs: 45,422 Avg rating: 3.2	—	—	4,042 major competitor apps
displayed	72	100	49	69 10,597 competing apps Median installs: 33,591 Avg rating: 2.7	—	—	886 major competitor apps
pattern	71	100	51	61 3,917 competing apps Median installs: 45,921 Avg rating: 3.2	—	—	453 major competitor apps
possible	70	100	53	75 23,344 competing apps Median installs: 31,057 Avg rating: 2.8	—	—	2,330 major competitor apps
decision	72	100	42	62 4,410 competing apps Median installs: 35,836 Avg rating: 2.9	—	—	470 major competitor apps
training	71	100	49	73 18,059 competing apps Median installs: 25,570 Avg rating: 2.8	—	—	1,576 major competitor apps
data	67	100	65	83 68,455 competing apps Median installs: 26,834 Avg rating: 2.7	—	—	6,589 major competitor apps

App Description

Artificial neural network in your pocket

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. Time-series support is also included. You can now predict output values when patterns represent time-ordered records.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.