Victoria Hamburg FC: Squad, Stats & Achievements in 2. Bundesliga

Overview / Introduction about the Team

The Victoria Hamburg football team is a prominent club based in Hamburg, Germany. Competing in the Bundesliga, the team has established itself as a formidable presence in German football. Founded in 1897, Victoria Hamburg has been under the guidance of their current coach since 2021. The team primarily plays with a 4-3-3 formation.

Team History and Achievements

Victoria Hamburg boasts a rich history with several notable achievements. They have won multiple regional titles and have consistently finished in the top half of the league standings. The most memorable season was 2018 when they secured third place in the league.

Current Squad and Key Players

The current squad features standout players such as Thomas Müller (Forward), who leads in goals scored, and Michael Reus (Midfielder), known for his assists. Key positions include a strong defensive line led by Captain Stefan Klose.

Team Playing Style and Tactics

Victoria Hamburg employs an aggressive 4-3-3 formation, focusing on high pressing and quick transitions. Their strengths lie in their offensive capabilities, while weaknesses are occasionally seen in defensive lapses.

Interesting Facts and Unique Traits

Nicknamed “The Lions,” Victoria Hamburg has a passionate fanbase known for their vibrant support. They have a fierce rivalry with FC Berlin, which often leads to intense matches filled with tradition.

Lists & Rankings of Players, Stats, or Performance Metrics

• Top Scorer: Thomas Müller – ✅
• Most Assists: Michael Reus – ✅
• Average Goals per Match: 1.8 – 💡
• Defensive Errors: Increased recently – ❌

Comparisons with Other Teams in the League or Division

Victoria Hamburg is often compared to FC Munich for their attacking prowess but falls short defensively against teams like Stuttgart.

Case Studies or Notable Matches

A breakthrough game was their victory over Bayern Munich last season, which highlighted their potential against top-tier teams.

Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds

Metric	Last Season	This Season (so far)
Total Wins	14	10
Total Goals Scored	45	30
Average Possession (%)	58%	55%
Odds for Next Match Win	– (N/A)

Tips & Recommendations for Analyzing the Team or Betting Insights

To analyze Victoria Hamburg effectively for betting purposes, focus on their recent form against direct competitors and consider betting on over 1.5 goals due to their offensive style.

Betting Tips:

• Analyze head-to-head records against key rivals.
• Bet on high-scoring games given their attacking playstyle.

Betting Insights:

• Carefully monitor player injuries before placing bets.
• Leverage odds shifts as indicators of market sentiment.

Betting Potential:

• Victoria’s home games typically offer favorable odds for bettors.

Analyzing Strengths:

• Focusing on key players’ performances can guide betting decisions effectively.

Analyzing Weaknesses:

• Paying attention to defensive vulnerabilities can help identify potential upset opportunities.

“Victoria Hamburg’s dynamic playstyle makes them unpredictable opponents.” – Expert Analyst John Doe

Making Betting Decisions: Pros & Cons of Current Form or Performance 📊✅❌🎰💡

• Strong attacking lineup increases chances of high-scoring games.
• Recent wins boost team morale and confidence.
• Experienced coach brings strategic depth.
• Defensive lapses could lead to unexpected losses.
• Injuries among key players pose significant risks.
• Consistency issues may affect long-term performance.
• Monitor injury reports closely before placing bets.
  </[0]: # -*- coding: utf-8 -*-
  [1]: # Copyright (c) 2015 Mirantis Inc.
  [2]: #
  [3]: # Licensed under the Apache License, Version 2.0 (the "License");
  [4]: # you may not use this file except in compliance with the License.
  [5]: # You may obtain a copy of the License at
  [6]: #
  [7]: # http://www.apache.org/licenses/LICENSE-2.0
  [8]: #
  [9]: # Unless required by applicable law or agreed to in writing, software
  [10]: # distributed under the License is distributed on an "AS IS" BASIS,
  [11]: # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  [12]: # See the License for the specific language governing permissions and
  [13]: # limitations under the License.

  [14]: import itertools

  [15]: from nailgun import consts
  [16]: from nailgun import exceptions
  [17]: from nailgun.logger import logger
  [18]: from nailgun.settings import settings

  [19]: def get_nodes_from_cluster(cluster):
  [20]: nodes = []
  [21]: if cluster.nodes:
  [22]: nodes = cluster.nodes.all()
  [23]: return nodes

  [24]: def get_roles_from_cluster(cluster):
  [25]: roles = []
  [26]: if cluster.roles:
  [27]: roles = cluster.roles.all()
  [28]: return roles

  [29]: def get_services_from_cluster(cluster):
  [30]: services = {}
  [31]: if cluster.services:
  [32]: services = {service.role.name: service.service_name for service in cluster.services.all()}

  def __get_active_roles_list(roles_list):

  active_roles_list = [role.name.lower() for role in roles_list if role.is_active]

  active_roles_list = __get_active_roles_list(roles)

  available_services_per_role = {
  'controller': ['corosync', 'pacemaker', 'vrouter'],
  'compute': ['nova-compute', 'vrouter'],
  'ceph-osd': ['rbd-mirror']
  }

  if settings.APPLY_RBD_MIRROR_ON_CEPH_OSD_ROLE:

  available_services_per_role['ceph-osd'].append('rbd-mirror')

  services_per_role = {}

  for role_name_lowered in active_roles_list:

  services_per_role.update({role_name_lowered: available_services_per_role.get(role_name_lowered)})

  services_per_role.update({'any': ['nailgun']})

  def __get_service_status(service_name):

  try:

  return {
  'status': getattr(services.get(service_name), 'status').lower(),
  'is_blocked_by_configuration_error': getattr(services.get(service_name), 'is_blocked_by_configuration_error')
  }

  except AttributeError:

  return {
  'status': consts.ServiceStatus.UNKNOWN,
  'is_blocked_by_configuration_error': False
  }

  def __get_all_services_status():

  all_services_status_dict = {}

  all_available_services_names = list(itertools.chain(*services_per_role.values()))

  for service_name_lowered in all_available_services_names:

  all_services_status_dict.update({
  service_name_lowered: __get_service_status(service_name_lowered)
  })

  return all_services_status_dict

  def __get_all_available_services_names():

  all_available_services_names_set = set()

  all_available_services_names_set.update(set(itertools.chain(*services_per_role.values())))

  return list(all_available_services_names_set)

  def __is_any_service_in_pending_state(all_services_status_dict):

  any_service_in_pending_state = False

  any_service_in_pending_state |= any([
  (
  service_status_dict['status'] == consts.ServiceStatus.PENDING_DEPLOYMENT.value.lower()
  )
  for service_status_dict in all_services_status_dict.values()
  ])

  return any_service_in_pending_state

  def __is_any_service_blocked_by_configuration_error(all_services_status_dict):

  any_service_blocked_by_configuration_error = False

  any_service_blocked_by_configuration_error |= any([
  (
  service_status_dict['is_blocked_by_configuration_error']
  )
  for service_status_dict in all_services_status_dict.values()
  ])

  return any_service_blocked_by_configuration_error

  def __has_rbd_mirror_failed(all_available_services_names):

  rbd_mirror_failed_flag_present_in_cluster_settings_flag = getattr(settings.CLUSTER_SETTINGS_DICT.get('cluster'), 'rbd_mirror_failed_flag_present', False)

  rbd_mirror_failed_on_some_nodes_flag_present_in_cluster_settings_flag = getattr(settings.CLUSTER_SETTINGS_DICT.get('cluster'), 'rbd_mirror_failed_on_some_nodes_flag_present', False)

  rbd_mirror_failed_on_all_nodes_flag_present_in_cluster_settings_flag = getattr(settings.CLUSTER_SETTINGS_DICT.get('cluster'), 'rbd_mirror_failed_on_all_nodes_flag_present', False)

  rbd_mirrors_are_enabled_and_configured_properly_and_have_not_been_deployed_yet_or_failed_on_some_node_or_all_nodes_flags_are_not_set_at_the_same_time_flag = (
  (
  settings.APPLY_RBD_MIRROR_ON_CEPH_OSD_ROLE
  ) and (
  not rbd_mirror_failed_flag_present_in_cluster_settings_flag
  ) and (
  not rbd_mirror_failed_on_some_nodes_flag_present_in_cluster_settings_flag
  ) and (
  not rbd_mirror_failed_on_all_nodes_flag_present_in_cluster_settings_flag
  )
  )

  rbd_mirrors_are_enabled_and_configured_properly_and_have_been_deployed_yet_or_have_been_deployed_but_failed_only_on_some_node_flags_are_not_set_at_the_same_time_flag = (
  (
  settings.APPLY_RBD_MIRROR_ON_CEPH_OSD_ROLE
  ) and (
  not rbd_mirror_failed_on_some_nodes_flag_present_in_cluster_settings_flag
  ) and (
  not rbd_mirror_failed_on_all_nodes_flag_present_in_cluster_settings_flag
  )
  )

  some_rbd_mirrors_have_been_deployed_and_succeeded_while_other_rbd_mirrors_have_been_deployed_and_falied_or_rbd_mirrors_are_enabled_and_configured_properly_but_haven't_been_deployed_yet_flags_are_not_set_at_the_same_time_or_both_of_them_are_not_set_at_the_same_time_flags_is_true_if_any_of_above_conditions_is_true_otherwise_it_is_false= (
  not(
  bool(rbd_mirrors_are_enabled_and_configured_properly_and_have_not_been_deployed_yet_or_failed_on_some_node_or_all_nodes_flags_are_not_set_at_the_same_time_flag) ^ bool(rbd_mirrors_are_enabled_and_configured_properly_and_have_been_deployed_yet_or_have_been_deployed_but_failed_only_on_some_node_flags_are_not_set_at_the_same_time_flag)
  )
  )

  some_rbd_mirrors_have_been_deployed_and_succeeded_while_other_rbd_mirrors_have_been_deployed_and_falied_or_rdb_mirrors_are_enabled_and_configured_properly_but_haven't_been_deployed_yet_flags_are_not_set_at_the_same_time_or_both_of_them_are_not_set_at_the_same_time_flags_is_true_if_any_of_above_conditions_is_true_otherwise_it_is_false=not(
  some_rbd_mirrors_have_been_deployed_and_succeeded_while_other_rdb_mirrors_have_been_deployed_and_falied_or_rdb_mirrors_are_enabled_and_configured_properly_but_haven't_been_deployed_yet_flags_are_not_set_at_the_same_time_or_both_of_them_are_not_set_at_the_same_time_flags_is_true_if_any_of_above_conditions_is_true_otherwise_it_is_false ^ bool(rdb_mirrored_disks_statuses_has_a_value_different_than_success_value)
  )

  some_ceph_osds_with_no_disks_to_be_replaced_exist_within_this_ceph_mon_group_that_has_already_achieved_quorum_for_a_given_osd_id_value_that_needs_to_be_replaced=False#TODO this value should be calculated by CephManagerService#get_mon_quorum_members_for_osd_id method instead of being hardcoded here.

  return some_ceph_osds_with_no_disks_to_be_replaced_exist_within_this_ceph_mon_group_that_has_already_achieved_quorum_for_a_given_osd_id_value_that_needs_to_be_replaced

  pass

  ***** Tag Data *****
  ID: 1
  description: Complex logic determining various states related to RBD mirrors within
  Ceph OSDs.
  start line: 40
  end line: 131
  dependencies:
  – type: Function
  name: get_clusters_from_roles_list__service_statuses__and_more__
  start line: 19
  end line: 131
  context description: This snippet is part of a larger function that collects various
  statuses related to roles within clusters. It handles complex logical conditions,
  including checking configuration errors and deployment statuses.
  algorithmic depth: 4
  algorithmic depth external: N
  obscurity: 4
  advanced coding concepts: 4
  interesting for students: 5
  self contained: N

  *************
  ## Suggestions for complexity

  1. **Dynamic Service Role Assignment**: Implement logic that dynamically assigns services to roles based on real-time metrics such as CPU usage or network latency rather than static predefined lists.

  2. **Hierarchical Service Dependencies**: Introduce hierarchical dependencies between services where certain services can only be active if other dependent services are also active.

  3. **Rollback Mechanism**: Add functionality that automatically rolls back changes if certain conditions fail during deployment checks.

  4. **Custom Error Handling**: Implement custom error handling mechanisms that provide detailed logs when configuration errors occur during status checks.

  5. **Concurrent Status Checks**: Modify the code to perform concurrent status checks using asynchronous programming techniques to improve performance.

  ## Conversation

  : I need help understanding this piece of code [SNIPPET]. Specifically how `rdb_mirrored_disks_statuses_has_a_value_different_than_success_value` is used later?

  : Sure! In your code snippet:

  python
  rdb_mirrored_disks_statuses_has_a_value_different_than_success_value=None#TODO this value should be calculated by CephManagerService#get_mon_quorum_members_for_osd_id method instead of being hardcoded here.

  This variable is intended to store whether there are mirrored disk statuses different from success but it’s currently set to `None`. Later:

  python
  some_rdb_mirrors_have_been_deployment…flags_is_true_if_any_of_above_conditions_is_true_otherwise_it_is_false^bool(rdb_mirrored_disks_statuses_has_a_value_different_than_success_value)

  This expression uses XOR (`^`) logic which means it will evaluate true only if one condition is true but not both.

  To make this functional you need an actual value calculation logic replacing `None`.

  Would you like me to implement how `CephManagerService#get_mon_quorum_members_for_osd_id` might calculate this value?

  : Yes please show me how I can replace None with actual calculation

  : Sure! Let’s assume we have access to `CephManagerService` which has a method `get_mon_quorum_members_for_osd_id`. We’ll use this method to replace `None` with an actual calculated value.

  Here’s how you might integrate it:

  Firstly define `CephManagerService` somewhere appropriate:

  python
  class CephManagerService:
  @staticmethod
  def get_mon_quorum_members_for_osd_id(osd_id):
  # Placeholder implementation; replace with actual logic.
  quorum_members_info = {
  “osd_0”: {“status”: “success”},
  “osd_1”: {“status”: “failed”},
  …
  }
  osd_info = quorum_members_info.get(osd_id)

  if osd_info:
  return osd_info[“status”] != “success”

  Then modify your main code snippet accordingly:

  python
  # Replace None assignment with actual calculation call.
  osd_ids_to_check = […] # List of OSD IDs relevant here.
  statuses_differs_from_success_values=[CephManagerService.get_mon_quorum_members_for_osd_id(osd_id) for osd_id in osd_ids_to_check]

  if any(statuses_differs_from_success_values): # If any status differs from success.
  rdb_mirrored_disks_statuses_has_a_value_different_than_success_value=True
  else:
  rdb_mirrored_disks_statuses_has_a_value_different_than_success_value=False

  some_ceph_osds_with_no_disks_to_be_replaced_exist_within_this_ceph_mon_group_that_has_already_achieved_quorum_for_a_given_osd_id_value_that_needs_to_be_replaced=False#TODO this value should be calculated by CephManagerService#get_mon_quorum_members_for_osd_id method instead of being hardcoded here.
  some_rdb…

  Now your variable `rdb_mirrored_disks_statuses_has_a_value_different_than_success_value` will hold an accurate boolean based on real data obtained from `CephManagerService`.

  Does this address your requirement? Do you need further modifications?
  1: DOI: 10.1016/j.mex.2020.100920
  2: # A novel approach towards developing reliable models using artificial neural networks based on random sampling methodology using MATLAB® software environment – case study on diesel engine combustion characteristics prediction model development using ANN models trained by random sampling methodology based optimization algorithm utilizing MATLAB® software environment – Case Study I – Part II☆☆☆☆☆☆☆☆☆☆☆☆☆☆☆★ ★ ★ ★ ★ ★ ★★★ ★ ★★★★★★★★★ ☆ ☆ ☆ ☆ ☆ ☆ ☆ ☆ ☆ ☆ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦ ♦♢♢♢♢♢♢♢♢♢♢♢♠▪▪▪▪▪▪▪▪▪▪◊◊◊◊◊◊◊◊◊●●●●●●●●■■■■■■■ ■■■ ■■ ■■ ■■ ■■ ■■ ■■ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ● ● ● ● ● ● ● ● ○ ○ ○ ○ ○ ○ ○ ⚫⚫⚫⚫⚫⚫⚫⚫⚫ ♂ ♂ ♂ ♂ ♂ ♂ ♂ ☼ ☼ ☼ ☼ ☼ ☼ ☼ ⊗ ⊗ ⊗ ⊗ ⊗ ⊗ ⊗ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ∆ ∆ ∆ ∆ ∆ ∆ ∆ ‖ ‖ ‖ ‖ ‖ ‖ ‖ ↔ ↔ ↔ ↔ ↔ ↔ ↔ ≈ ≈ ≈ ≈ ≈ ≈ ─ ─ ─ ─ ─ ─ ─ • • • • • • •        ¬ ¬ ¬ ¬ ¬ ¬ ¬ → → → → → → → + + + + + + + | | | | | | | ← ← ← ← ← ← ← ≥ ≥ ≥ ≥ ≥ ≥ ≥ ≤ ≤ ≤ ≤ ≤ ≤ ≤ × × × × × × × ≠ ≠ ≠ ≠ ≠ ≠ ≠ ° ° ° ° ° ° ° £ £ £ £ £ £ £ § § § § § § § ¤ ¤ ¤ ¤ ¤ ¤ ¤ ¶ ¶ ¶ ¶ ¶ ¶ ¶ Ω Ω Ω Ω Ω Ω Ω ¥ ¥ ¥ ¥ ¥ ¥ ¥ ∀ ∀ ∀ ∀ ∀ ∀ ∀ µ µ µ µ µ µ µ ϕ ϕ ϕ ϕ ϕ ϕ ϕ ∧ ∧ ∧ ∧ ∧ ∧ ∧ ∈ ∈ ∈ ∈ ∈ ∈ ∈ ∨ ∨ ∨ ∨ ∨ ∨ ∨ ⊂ ⊂ ⊂ ⊂ ⊂ ⊂ ⊂ π π π π π π π τ τ τ τ τ τ τ φ φ φ φ φ φ φ λ λ λ λ λ λ λ α α α α α α α β β β β β β β γ γ γ γ γ γ γ δ δ δ δ δ δ δ ε ε ε ε ε ε ε θ θ θ θ θ θ η η η η η η κ κ κ κ κ κ κ μ μ μ μ μ μ μ ν ν ν ν ν ν σ σ σ σ σ σ σ χ χ χ χ χ χ ź ź ź ź ź ź į į į į į į ś ś ś ś ś ś Ś Ś Ś Ś Ś Ś š š š š š š ß ß ß ß ß ß Ç Ç Ç Ç Ç Ç Đ Đ Đ Đ Đ Đ É É É É É É Ï Ï Ï Ï Ï Ï Ö Ö Ö Ö Ö Ö Ü Ü Ü Ü Ü Ü å å å å å å æ æ æ æ æ æ ç ç ç ç ç ç đ đ đ đ đ đ é é é é é é í í í í í í ó ó ó ó ó ó ú ú ú ú ú ú Å Å Å Å Å Å Ø Ø Ø Ø Ø Ø à à à à à à è è è è è è î î î î î î ø ø ø ø ø ø ¿ ¿ ¿ ¿ ¿ ¿ © © © © © © ® ® ® ® ® ® ™ ™ ™ ™ ™ ™ ℕ ℕ ℕ ℕ ℕ ℕ œ œ œ œ œ œ « « « « « « » » » » » » · · · · · · … … … … … … — — — — — — – – – – – – : : : : : ; ; ; ; ; ‘ ‘ ‘ ‘ ‘ ’ ’ ’ ’ “ “ “ “ ” ” ” ” , , , , . . . / / / | | | | | | | | | | || || || || || || || || && && && && && && && && & & & & _ _ _ _ _ _ _ _ _ _ _
  3: Authors: M.S.S.Mohamed-Aziz-Mohamed-Azlan-Kamarudin-Salehan-Nur-Eddin-Mohamed-Azlan-Kamarudin-Salehan-Nur-Eddin-Mohamed-Aziz-Mohamed-Azlan-Kamarudin-Salehan-Nur-Eddin-Mohamed-Azlan-Kamarudin-Salehan-Nur-Eddin-Mohamed-Aziz-Mohamed-Azlan-Kamarudin-Salehan-Nur-Eddin-, Zainal-Zaidi-Shafie-Hanafi-Yousif-Omar-Al-Ghamdi-Hassan-Benmoussa-Lotfi-Boukhemacha-Wafa-Hamdi-Oussama-Belhadj-Hocine-Chahbani-Sami-Yazid-Tarik-Ibrahem-Benabdallah-Fatima-Zahra-Benabdelkader-Tariq-Alami-Chaibaa-Belkhodja-Tariq-Alami-Chaibaa-Belkhodja-Tariq-Alami-Chaibaa-
  4: Journal: MethodsX™ b∞∞∞∞∞∞∞∞∞∞∞∞∞∞*σσσσσσσσσ**πππππππ*ΣΣΣΣΣΣ**ωωωωωωω*††††††††††††*§§§§§§§***φφφφφφφ**ψψψψψψ*λλλλλλλ**αααααα**ββββββ**γγγγγγ**δδδδδδ**εεεεεε**θθθθθθ**ιιιιιι**κκκκκκ**μμμμμμ**
  5: Date Published:: January2020DOI:: https://doi.org/10/bnqqvgKeywords:: ANN ModelsTrained By Random Sampling Methodology Based Optimization Algorithm Utilizing Matlab Software EnvironmentDiesel Engine Combustion Characteristics Prediction Model DevelopmentUsing Artificial Neural Networks Based On Random Sampling Methodology Using Matlab Software EnvironmentCase StudyIPartIIANNmodelsTrainedByRandomSamplingMethodologyBasedOptimizationAlgorithmUtilizingMatlabSoftwareEnvironmentDieselEngineCombustionCharacteristicsPredictionModelDevelopmentUsingArtificialNeuralNetworksBasedOnRandomSamplingMethodologyUsingMatlabSoftwareEnvironmentCaseStudyIPartIIANNmodelsTrainedByRandomSamplingMethodologyBasedOptimizationAlgorithmUtilizingMatlabSoftwareEnvironmentDieselEngineCombustionCharacteristicsPredictionModelDevelopmentUsingArtificialNeuralNetworksBasedOnRandomSamplingMethodologyUsingMatlabSoftwareEnvironmentCaseStudyIPartIIANNmodelsTrainedByRandomSamplingMethodologyBasedOptimizationAlgorithmUtilizingMatlabSoftwareEnvironmentDieselEngineCombustionCharacteristicsPredictionModelDevelopmentUsingArtificialNeuralNetworksBasedOnRandomSamplingMethodologyUsingMatlabSoftwareEnvironmentCaseStudyIPartIIANNmodelsTrainedByRandomSamplingMethodologyBasedOptimizationAlgorithmUtilizingMatlabSoftwareEnvironmentDieselEngineCombustionCharacteristicsPredictionModelDevelopmentUsingArtificialNeuralNetworksBasedOnRandomSamplingMethodologyUsingMatlabSoftwareEnvironmentCaseStudyIPartIIANNmodelsTrainedByRandomSamplingMethodologyBasedOptimizationAlgorithmUtilizingMatlabSoftwareEnvironmentDieselEngineCombustionCharacteristicsPredictionModelDevelopmentUsingArtificialNeuralNetworksBasedOnRandomSamplingMethodologyUsingMatlabSoftwareEnvironmentCaseStudyIPartIIANNmodelsTrainedByRandomSamplingMethodologyBasedOptimizationAlgorithmUtilizingMatlabSoftwareEnvironmentDieselEngineCombustionCharacteristicsPredictionModelDevelopmentUsingArtificialNeuralNetworksBasedOnRandomSamplingMethodologyUsingMatlabSoftwareEnvironmentCaseStudyIPartIIANNmodelsTrainedByRandomSamplingMethodologyBasedOptimizationAlgorithmUtilizingMatlabSoftwareEnvironmentDieselEngineCombustionCharacteristicsPredictionModelDevelopmentUsingArtificialNeuralNetworksBasedOnRandomSamplingMethodologyUsingMatlabSoftwareEnvironmentCaseStudyIPartIIANNmodelsTrainedByRandomSamplingMethodologyBasedOptimizationAlgorithmUtilizingMatlabSoftwareEnvironmentDieselEngineCombustionCharacteristicsPredictionModelDevelopmentUsingArtificialNeuralNetworksBasedOnRandomSamplingMethodologyUsingMatlabSoftwareEnvironmentCaseStudyIPartIIANNmodelsTrainedByRandomSamplingMethodologyBasedOptimizationAlgorithmUtilizingMatlabSoftwareEnvironmentDieselEngineCombustionCharacteristicsPredictionModelDevelopmentUsingArtificialNeuralNetworksBasedOnRandomSamplingMethodologyUsingMatlabSoftwareEnvironmentCaseStudyIPartIIMethodDescriptionData AcquisitionData PreprocessingData MiningDeveloping Reliably Predictive Models Using Artificial Neural Networks Trained By Random Sampling Method Based Optimization Algorithm Utilizing Matlab Software EnvironmentValidation Of Developed ModelsPerformance EvaluationOf Developed ModelsConclusionReferencesAPPENDIX AAppendix BAppendix CAppendix DAppendix EAppendix FAppendix GAppendix HAppendix IAPPENDIX JAPPENDIX KAPPENDIX LREFERENCES APPENDIX MREFERENCES APPENDIX NREFERENCES APPENDIX OREFERENCES APPENDIX PREFERENCES APPENDIX QACKNOWLEDGEMENTSDisclaimerAbout The AuthorsBiographiesReferencesAppended ReferencesFigures Tables Symbols GlossaryAbbreviationsIndexAcknowledgmentsDisclaimersAbout The AuthorsBiographiesFigures Tables Symbols GlossaryAbbreviationsIndexAcknowledgmentsDisclaimersAbout The AuthorsBiographiesFigures Tables Symbols GlossaryAbbreviationsIndexAcknowledgmentsDisclaimersAbout The AuthorsBiographiesFigures Tables Symbols GlossaryAbbreviationsIndexAcknowledgmentsDisclaimersAbout The AuthorsBiographiesFigures Tables Symbols GlossaryAbbreviationsIndexAcknowledgmentsDisclaimersAbout The AuthorsBiographiesFigures Tables Symbols GlossaryAbbreviationsIndexAcknowledgmentsDisclaimersAbout The AuthorsBiographiesFigures Tables Symbols GlossaryAbbreviationsIndexAcknowledgmentsDisclaimersAbout The AuthorsBiographiesFigures TablesSymbolsGlossaryAbbreviationsIndexAcknowledgmentDisclaimerAboutTheAuthorsBiographyFigureTableSymbolGlossaryAbbreviationIndexAcknowledgeDisclaimerAuthorBioFigureTableSymbolGlossaryAbbreviationIndexAcknowledgeDisclaimerAuthorBioFigureTableSymbolGlossaryAbbreviationIndexAcknowledgeDisclaimerAuthorBioFigureTableSymbolGlossaryAbbreviationIndexAcknowledgeDisclaimerAuthorBioFigureTableSymbolGlossaryAbbreviationIndexAcknowledgeDisclaimerAuthorBioFigureTableSymbolGlossaryAbbreviationIndexAcknowledgeDisclaimerAuthorBioFigureTableSymbolGlossaryAbbreviationIndexAcknowledgeDisclaimerAuthorBioFigureTableSymbolGlossaryAbbreviationIndexAcknowledgeDisclaimerAuthorBioFigureTableSymbolGlossary Abbreviation Index Acknowledgement Disclaimer About Author Biography Figure Table Symbol Gloss ary Abb revia tion Index Acknowledgement Disclaimer About Author Biography Figure Table Symbol Gloss ary Abb revia tion Index Acknowledgement Disclaimer About Author Biography Figure Table Symbol Gloss ary Abb revia tion Index Acknowledgement Disclaimer About Author Biography Figure Table Symbol Gloss ary Abb revia tion Index Acknowledgement Disclaimer About Author Biography Figure Table Symbol Gloss ary Abb revia tion Index Acknowledgement Disclaimer About Author Biography Figure Table Symbol Gloss ary Abb revia tion Index ACKNOWLEDGMENT DISCLAIMER ABOUT THE AUTHORS BIOGRAPHIES FIGURE TABLE SYMBOL GLOSSARY ABBREVIATION INDEX ACKNOWLEDGE DISCLAIMER AUTHOR BIO FIGURE TABLE SYMBOL GLOSSARY ABBREVIA TION INDEX ACKNOWLEDGE DISCLAIMER AUTHOR BIO FIGURE TABLE SYMBOL GLOSSARY ABBREVIA TION INDEX ACKNOWLEDGE DISCLAIMER AUTHOR BIO FIGURE TABLE SYMBOL GLOSSARY ABBREVIA TION INDEX ACKNOWLEDGE DISCLAIMER AUTHOR BIO FIGURE TABLE SYMBOL GLOSSARY ABBREVIA TION INDEX ACKNOWLEDGE DISCLAIMER AUTHOR BIO FIGURE TABLE SYMBOL GLOSSARY ABBREVIA TION INDEX ACKNOWLEDGE DISCLAIMER AUTHOR BIO FIGURE TABLE SYMBOL GLOSSARY ABBREVIA TION INDEX ACKNOWLEDGE DISCLAIMER AUTHOR BIOFIGURETABLESYMBOLGLOSSARYABBREVIATIONINDEXACKNOWLEDGEDISCLAIMERAUTHORBIOFIGURETABLESYMBOLGLOSSARYABBREVIATIONINDEXACKNOWLEDGEDISCLAIMERAUTHORBIOFIGURETABLESYMBOLGLOSSARYABBREVIATIONINDEXACKNOWLEDGEDISCLAIMERAUTHORBIOFIGURETABLESYMBOLGLOSSARYABBREVIATIONINDEXACKNOWLEDGEDISCLAIMERAUTHORBIOFIGURETABLESYMBOLGLOSSARYABBREVIATIONINDEXACKNOWLEDGEDISCLAIMERAUTHORBIOFIGURETABLESYMBOLGLOSSARYABBREVIATIONINDEXACKNO WLEDGEDISCLAIMERAUTHORBIOSummary TextA novel approach towards developing reliable models using artificial neural networks based on random sampling methodology using MATLAB® software environment case study on diesel engine combustion characteristics prediction model development using ANN models trained by random sampling methodology based optimization algorithm utilizing MATLAB® software environment case study I part II [M.S.S.Mohamed Aziz Mohamed Azlan Kamarudin Salehan Nur Eddin Mohamed Azlan Kamarudin Salehan Nur Eddin Mohamed Aziz Mohamed Azlan Kamarudin Salehan Nur Eddin Mohamed Azlan Kamarudin Salehan Nur Eddin]A novel approach towards developing reliable models using artificial neural networks based on random sampling methodology using MATLAB® software environment case study on diesel engine combustion characteristics prediction model development using ANN models trained by random sampling methodology based optimization algorithm utilizing MATLAB® software environment case study I part II [M.S.S.Mohamed Aziz Mohamed Azlan Kamarudin Salehan Nur Eddin Mohamed Azlan Kamarudin Salehan Nur Eddin Mohamed Aziz Mohamed Azlan Kamarudin Salehan Nur Eddin Mohamed Azlan Kamarudin Salehan Nur Eddinzainal zaidi shafie hanafi youssif omar al ghamdi hassan benmoussa lotfi boukhemacha wafa hamdi oussama belhadj hocine chahbani sami yazid tarik ibrahem benabdallah fatima zahra benabdelkader tariq alami chaiba ab elkhodja tariq alami chaiba ab elkhodja tariq alamimethod descriptiondata acquisitiondata preprocessingdataminingdeveloping reliably predictive modelswith artificial neural networks trainedbyrandomsamplingmethodbasedoptimizationalgorithmutilizingmatlabssoftwareenvironmentvalidationofdevelopedmodelspereformance evaluationof developed modelsconclusionreferencesappendix appendixaappendix bappendix cappendix dappendix eappendix fappendix gappendix happendix iappend ix j append ix k append ix l references appendix mreferences appendix nreferences appendix oreferences appendix p references appended referencesfigures tables symbols glossar yabbreviationsindexackno wledgedisclaimerabouttheauthorsbiographyfiguretablesymbolglossarya bbre viationindexackno wledgedisclaimerabouttheauthorsbiographyfiguretablesymbolglos sa ry abbreviatio nindexackno wledgedisclaimerabouttheauthorsbiographyfiguretablesymbolglos sa ry abbreviatio nindexackno wledgedisclaimerabouttheauthorsbiographyfiguretablesymbolglos sa ry abbreviatio nindexackno wledgedisclaimerabouttheauthorsbiographyfiguretablesymbolglos sa ry abbreviatio nindexackno wledgedisclaimerabouttheauthorsbiographyfiguretablesymbolglos sa ry abbreviatio nindexackno wledgedisclaimerabouttheauthorsbiographyfiguretablesymbolglos sa ry abbreviatio nindexackno wledgedisclaimerabouttheauthorsbiography figure table symbol gloss ary abbreviation index ack no wledge disclaimer about author bio figure table symbol gloss ary abbreviation index ack no wledge disclaimer about author bio figure table symbol gloss ary abbreviation index ack no wledge disclaimer about author bio figure table symbol gloss ary abbreviation index ack no wledge disclaimer about author bio figure table symbol gloss ary abbreviation index ack no wledge disclaimer about author bio figure table symbol gloss ary abbreviation index ack no wledge disclaimer