To find a solution for realistic complex problems, soft computing was introduced. In other words, it is nothing but an intersection point of various software techniques to solve many real-time issues. It doesn’t involve hard mathematical functions or strict constraints. Moreover, it tries to replicate the human mind while taking effective decisions. Majorly, it is well-suited for solving problems of approximation and uncertain situations. 

This article provides you with a detailed summary of the Soft Computing Field with up-to-date innovative areas and trends!!!

Our resource team has a long-lasting practice in implementing Soft Computing Projects. To present you with more innovative research ideas, we regularly refer to many recent reputed journals of soft computing for citing the references. Besides, we also check out several current research magazines and articles. Here, we have given you some important reputed journals of soft computing for your reference. 

Significant Journals for Soft Computing

• Applied Soft Computing
• IEEE Computational Intelligence Society
• Journal of Soft Computing
• World Global Conferences on Soft Computing

Now, we can see that the main reasons to select soft computing as a research field. As a matter of fact, soft computing is nothing but a type of artificial intelligence. The main aim of soft computing is to provide solutions to complicated issues which do not have enough information about the issues themselves. It effectively makes a decision like a real human by analyzing the situation and available issues details. Further, it also technically simplifies the mathematical operations.

Why to use Soft Computing approaches?

• Flexible to replace the complex traditional analytical and mathematical techniques
• Easy to work with systems that works based on mathematical model

Now, we can see the working principle of soft computing. It mainly utilizes techniques that are strong against partial truth, approximation, uncertainty, etc. Further, soft computing uses different core techniques to handle complex mathematical operations where some of them are given below,

What are the various techniques of soft computing?

• Artificial Neural Networks
• Machine Learning
• Fuzzy Logic
• Genetic Algorithms
• Probabilistic Reasoning
• Neural Computing

Generally, the techniques used for soft computing are essential to include the following properties. The above-specified techniques are sure to have all these properties to meet the soft computing requirements. Particularly, these properties help you to achieve the expected results at the time of techniques implementation. From our experience, our developers are adept to recognize the suitable techniques for your proposed research problem..

Properties of Soft Computing

• Ability to learn independently
• Use unproven theorems
• Used for non-linearity
• Robust against Estimation, Ambiguity, Inaccuracy, Partial Truth
• Fault tolerant over Errors / Noise
• Assurance of Low-cost solution, Robustness and Tractability
• Work like human thinking for reasoning than conventional approaches

Premises of soft computing

• Provides the precision for input features and solves uncertainty issues
• Able to solve all the real-time issues by accurate decision making.

In addition, we have given you the impact of soft computing on real-world problems. It implicitly mentions the uses of soft computing. These uses attract active research scholars to do research on the soft computing field. 

Our developers have abundant knowledge on handling different varieties of Soft Computing Projects to create the best contribution to social developments. So, connect with us to create your incredible contribution to the soft computing research field.

Implications of Soft Computing

• It is largely used in industrial applications and other automated systems
• It uses fuzzy logic, neural network, support vector machine, etc.as harmonizing techniques
• It also uses hybrid techniques like neurofuzzy

As mentioned earlier, soft computing majorly focuses on real-world scenarios. Since is mainly proposed for solving real-world problems. So, this field is extensively spread in many application areas. Here, we have given you the list of top-trending soft computing techniques and applications for your reference. If you are curious to know the different research perspectives of real-time soft computing projects, then communicate with us. We will provide you with more useful information in your suggested research areas.

Soft Computing Techniques and Applications

• Fuzzy Logic – Used for achieving best results in approximate reasoning and uncertain problems
  • Applications
    • Smart Traffic Control in Highway System
    • Object detection in Underwater Network
    • Altitude Management System for Aerospace
• Non Linear Predictors – Used for Optimizing and Predicting Model Behavior
  • Applications
    • Aerospace Application
    • Chemical Engineering Application
• Evolutionary Approach – Used for optimization
  • Applications
    • Design of Computer-aided System
• Harmony Approach – Used for Search Operation in Real-Time Issues
  • Applications
    • Optimal Pattern Detection Techniques for Image Processing
    • Optimization of Telecommunication Services
    • Quality Improvement in Speech Tagging
    • Design of WSN applications
• Genetic Approach – Used for searching specific data from large datasets
  • Applications
    • Automated Robots Modeling
    • Routing Optimization for Telecommunication
    • Hardware Design and Engineering
    • Shipment and Traffic Management
• Rough Sets – Used for similar pattern restructuring and detection
  • Applications
    • Pattern Detection and Classification
    • Emotion Analysis in Image Processing
    • Gene Expression for Data Analysis
    • Defective System Identification
    • Integration of Remote Sensing in Image Processing
    • Power System Control in Industries
    • Decision Making for Medical Treatment

From the above list of techniques, we have taken “Fuzzy Logic” as an example for illustrating various methods used for soft computing project. Since the selection of technique is a more important step in project development for any sort of application. Here, we have given the best result-yielding techniques specifically for fuzzy-related soft computing projects. Once you create a bond with us, our developers will whole responsibility of selecting appropriate techniques for your project.

Different Fuzzy Methods for Soft Computing

• Formation of Fuzzy Rule
  • Rules Creation
  • Rules Aggregation
  • Rules Decomposition
• Fuzzification
  • Angular Fuzzy sets
  • Genetic Algorithm
  • Inference Rank Ordering
  • Neural Networks
  • Inductive Reasoning
• Fuzzy-based Decision Making
  • Fuzzy Bayesian
  • Multi-person / Individual
  • Multi- attribute / Multi-objective
• Defuzzification
  • Maxima (First and Last)
  • Centroid function
  • Alpha-cuts
  • Mean-Max membership
  • Centre of Huge area
  • Weight average function
  • Centre of Sum
• Fuzzy-based Inference Systems
  • Sugeno FIS Computation
  • Mamdani
• Fuzzy Measurements
  • Probability
  • Belief
  • Plausibility

Next, we can see two primary branches of soft computing such as classification and clustering. These operations are mainly used to characterize the object in terms of different features. Firstly, classification is used to allocate the objects to a specific group/class. Here, the classification groups/classes are decided by the object measurements. For that, it requires a supervised learning approach, labels, and “rule” for new points labeling. 

Secondly, clustering is used to group the monitored data based on similar patterns. For that, it requires unsupervised learning, grouping the nearer points to form the cluster, no need for labels, As well, it detects the structure of data. Below, we have given some major techniques used for both classification and clustering operations. 

Soft Computing Techniques

• Classification Techniques
  • Instance-based Learning
    • Nearest Neighbor
  • Perception-based Learning
    • Radial Basis Function
    • Single-layer Perception
    • Multi-Layer Perception
  • Supervised Learning
    • Support Vector Machine
    • Naïve Bayes
    • J48 Decision Tree
  • Statistical Learning
    • Instance-based
    • Naïve Bayes
    • Bayesian Networks
  • Unsupervised Learning
    • K-means-based Sense Clusters
• Clustering Techniques
  • Optimization Approaches
    • Simulated Annealing
    • Fuzzy Clustering
    • Evolutionary Algorithms
  • Partitioning Approaches
    • Graph Theory
    • Error Minimization
  • Model-based
    • Neural Network
    • Decision Trees
  • Hierarchical Approaches
    • Single-link
    • Average-link
    • Complete-link
    • Agglomerative Hierarchy
    • Divisive Hierarchy

Now, we can see about creative research areas of soft computing. All these areas are collected on the basis of the current research scholars’ interests. Our technical legends have in-depth knowledge in all these areas to support you in every aspect. Beyond this list of areas, we also support you in other emerging research areas of soft computing. We assure you that we mainly concentrate latest research demands of soft computing.

Innovative Research Ideas in Soft Computing 

• Computer Vision
• Soft Computing
• 3D Image Processing
• Neuro Fuzzy System
• Power System and Architecture
• Speech Recognition Models
• Decision Making Services
• Automated Manufacturing Systems 
• Large-Data Compression and Decompression

Additionally, we have also given you the current trends to make you aware of the current research directions of soft computing. Initially, soft computing was largely utilized in image processing and pattern recognition areas. Then, it is gradually held hand with many other areas due to its potentiality to solve real-world problems. In this way, it is currently reaching its peak point of success in the following subject areas.

Project Topics in Soft Computing Projects

• Moving Object Detection
• Digital Forensic Security
• Web Mining
• Data Mining
• GPS-based Target Tracking
• Facebook Face Identification
• Medical Disorder Analysis
• Remote Sensed Data Investigation
• Human-to-Machine Transmission
• Digital Signature Matching
• Multimedia Processing and Visualization
• Optical and Handwritten Character Detection

For a better understanding of the soft computing projects, here we have given you the flow of work in soft computing models. For that, we have taken the “Medical Disorder Analysis” topic from the above list. In this, we have given you the steps involved to classify and diagnose disease using medical input data. Further, we have also included the possible algorithms and techniques for training datasets that give desired project outcome. Likewise, we also give you an implementation plan for your handpicked project topic.

Workflow of Soft Computing Models

• Step 1 – Collect raw input data as train and test datasets
• Step 2 – Perform preprocessing techniques to clean the data by the followings,
  • Reduce the dimension
  • Rebalance the data
  • Remove missing value
  • Harmonize the data
• Step 3 – Train the data using learning techniques for feature selection, classification and regression processes. Some sample algorithms are given as follows,
  • Feature Selection Algorithms
    • Random Forest
    • RelieF
    • Elastic net
    • Correlation-based
    • Lasso
    • Gradient Boosting
• Step 4 – While training, also perform external and internal verification using leave-one-out / cross-validation / discrimination and calibration
• Step 5 – Test the prediction model. For that, get the new raw data (patient detail) as input and achieve the output of risk analysis, disease classification and diagnosis

For the development of soft computing projects, the selection of development tools, libraries, and API are more important. Since all these play a major role in practically that prove your handpicked research solutions over research problem. If you are new to this field, then it is best to get the expert’s guidance like us. As well, we provide excellent assistance in selecting optimal development tools, API, packages, libraries, toolboxes, and modules.

Tools, Libraries, and APIs for soft computing

• Matlab Toolboxes and Tools
• Hadoop Frameworks
• Jfuzzylite Fuzzy Java library
• Scilab Toolboxes and Tools
• Fuzzylite Fuzzy C++ library
• R and Weka programming tools
• Python Packages and Libraries

Once we undergo a deep review of your project objectives. We suggest you necessary development requirements. Here, we match the objectives with key capabilities of a development tool, API, packages, libraries, toolboxes, and modules to choose the optimal one for your project. For illustration purposes, here we have given you a supportive package of the R tool. Similar to this, we also do an assessment on other tools. For instance, check out some of the packages of R, 

R Programming Packages for Soft Computing

• Fuzzy Logic
  • frbs – It is used to execute classification and regression operations based on fuzzy-rule
• Neural Networks
  • RSNNS – It act as interface for Stuttgart Neural Network Simulator (SNNS)
  • klaR – It is used for classification. For instance: SVMlight
  • AMORE – It is used to apply TAO for efficient neural network
  • Rdetools – It is used for prediction and selection of features
  • nnet – It is used to identify single hidden layer in neural network
  • kernlab – It is used for kernel learning. For instance: RVM and SVM
• Genetic Algorithm
  • Rmalschains – It is used to perform local search along with genetic algorithm for optimizing tru value
  • Rgp and rgenoud – It is used to implement genetic algorithm

To the end, we assure you that we provide complete development support on your soft computing projects starting from research topic selection to thesis/dissertation submission. Specifically, we provide guidance on every step of your soft computing research journey. Overall, our development results surely satisfy your project requirements in all aspects. As well, this service is common for both active research scholars and final year students. So, if you are interested to join your hands to create the best project then connect with us.