Soft computing is an approach for handling real-world problems through the set of input features and making the decisions accordingly. In simple terms, soft computing has functions similar to the human mind which frequently changes. Majorly, it has uncertain characteristics. 

From this article, you can collect the latest information on soft computing project ideas, techniques, performance parameters, etc. with other fundamentals!!!

Before that, computing is classified into two types as Hard Computing and Soft Computing. On the one hand, hard computing has certain limitations to perform its operations. On the other hand, soft computing has no restriction which performs independently by all means. 

Soft Computing vs. Hard Computing

• Soft Computing
  • Computation Type – Parallel
  • Computation Duration – Low
  • Dependency – Based on Dispositional and Approximation
  • For Instance – Neural Networks (Art Networks, Adaline, Madaline, etc.)
  • Outcome – Approximate
• Hard Computing
  • Computation Type – Sequential
  • Computation Duration – High
  • Dependency – Based on Numerical and Logical System
  • For Instance – Numerical Issues that are solved by computers
  • Outcome – Accurate

Soft Computing Overview

In this, we can see how soft computing has emerged. In point of fact, soft computing has origin in artificial intelligence. Since artificial intelligence is classified into two types of computing where one is hard computing and the other is soft computing. 

Here, hard computing solves the problems on the basis of the conventional numerical model, search, and symbolic logic reasoning. Likewise, soft computing solves the problems on the basis of randomized search and approximate reasoning. Further, the AI-based hard and soft computing techniques have the following features, 

• Hard Computing
  • Categorical and Accuracy
  • Firmly Written Programs
  • Precise Approaches / Algorithms
  • Accurate Solution
  • Accurate Input data
  • Exact Analytical Model
• Soft Computing
  • Approximation
  • Adaptable and Trainable
  • Heuristic Approaches
  • Approximate Solution
  • Inaccurate / Noisy Input data
  • Estimated Model

It uses the probability concept in the case of criticality in problem-solving. If there is a lack of information in solving a problem, then soft computing plays a major role in determining suitable solutions. Even though you don’t have information about the problem itself, it is efficient to identify the hidden facts in available information. So, it turns out to be an interesting research platform for current research people who looking for innovations. 

What are objectives of soft computing approaches?

• To attain effective automated decisions similar to human decisions
• To develop solutions for real-time problems through smart machines
• To provide robustness against uncertainty, partial truth, inaccuracy, approximation, etc.
• To simplify the development by minimizing mathematical operations

Now, we can see a few important characteristics of soft computing. These characteristics influence soft computing to grow faster than other technologies. Moreover, these unique characteristics provide innovative research ideas for soft computing projects. Also, these characteristics are completely different from hard computing which focuses merely on approximation. Our research team has developed numerous soft computing project ideas by highlighting these characteristics from the latest research areas. 

What are Characteristics of Soft Computing?

• Ability to learn independently
• Enables to perform smart computations
• Allows to made decisions on critical situations like human
• Adaptable to work with continuous varying environment
• Empower to make expertize in specific domain area

Due to the numerous benefits of soft computing, it widely extends in several areas particularly in the Internet of Things (IoT). Since the soft computing concept is highly utilized for developing smart applications and systems. Therefore, technically it has many intelligent techniques and approaches are present for developing effective soft computing project ideas. Through these techniques, one can identify the IoT concepts. In recent times, several ultra-fast microcontrollers are managing the majority of soft computing applications at minimum cost.   

Next, we can see the main classifications of soft computing. This classification mainly addresses the important techniques of soft computing. Our developers are proficient enough to attain expected results from these techniques/algorithms. Beyond these techniques, we also have experience in other emerging research solutions. We are good not only at handling pre-defined algorithms but also at developing new algorithms. Depending on the project motives, we suggest best-fitting research solutions for your selected research problem of soft computing. Only after the deep study of your research problem, we give our solution recommendation. 

Taxonomy of Soft Computing

• Evolutionary Methods
  • Evolutionary Approach
  • Genetic Algorithm
  • Differential Evolution
• Fuzzy System
  • Fuzzy Theory
  • Fuzzy Set
  • Fuzzy Logic
• Swarm Intelligence
  • Fiefly Technique
  • PSO and ACO
• Neural Network
  • Feed Forward Neural Network
  • Artificial Neural Network
  • Back Propagation Neural Network
• Add-on Approaches
  • Choas Theories
  • Belief Theories
  • Wisdom Model

For illustration purposes, here we have taken the “Fuzzy System” from the above-listed techniques. Generally, the Fuzzy system has a key player role in developing advanced smart machines in the industrial sector. As well, it is also used to enhance bio-informatics in all possibilities. Primarily, it works effectively over real-time problems which are not solved by conventional techniques. Overall, a fuzzy system is more apt for creating innovation in the industrial sector by applying soft computing features. Some of the main features are a high proportion of machine intelligence, tractability, lower cost, etc. In addition, we have also given you the need for soft computing in the below section.

Purposes of Soft Computing

• Fuzzy Computing
  • Risk Evaluation
  • Design of Control Model
  • Shaping of Ambiguous Input
• Learning Detection
  • Regression
  • Clustering
  • Classification
• Evolutionary Computing Algorithms
  • Non-convex Optimization
  • Multi-Model Optimization
  • Multi-objective Optimization

Now, let’s see how the Evolutionary Computing Algorithms enhance the processes of soft computing. One special thing about this kind of algorithm is bio-inspiration. That is, it provides computation approaches by the inspiration of biological evolution. Moreover, it mostly functions with algorithms of meta-heuristic optimization like swarm intelligence and evolutionary methods. Similarly, we also know the purpose of other emerging algorithms. If two or more algorithms are focusing on the same objectives, then we are capable to choose the optimal one among them by comparing the performance of every algorithm. 

Our resource teams are adept to identify the latest research gaps and framing innovative soft computing project ideas. We assure you that our topics are unique which have a strong basement for future work. Here, we have given you a few significant research notions from our latest collections. Similarly, we also have countless advanced projects topics from every current research area of soft computing.

Top 13 Interesting Soft Computing Project Ideas 

• Smart Grid Load Prediction
• Link and Software Reliability Estimation
• Fault Detection and Diagnosis in Cyber System
• Intrusion Prevention System in Vehicular Network
• Software Fault Correction Duration Prediction
• Intrusion Stoppage in Networked Controllers
• Self-Organizing Map for SCADA-based Sensor Network
• Cyber-Attacks and Anomalies Recognition in Cyber System
• All-Terminal Network Reliability Computation
• Fault Localization and Classification in Power Transmission
• Intrusion Identification with Response Injunction for SCADA
• ANN-based Reliability Enhancement in Telecommunication
• Object Tracking and Detection using PSO Algorithm

To exhibit our development service, here we have taken “Object Tracking and Detection using PSO Algorithm” as an example one from the above-specified project ideas. Usually, our developers provide you project development plan for your handpicked project title. In this plan, we describe the implementation steps, basic system requirements in both hardware and software aspects. Further, we also include the list of performance measuring metrics that are used for examining project efficiency. This development service is common for all our handhold scholars and final-year students. Now, let’s see the development steps of our handpicked project topic.

Implementation Steps for Object Detection and Tracking using Soft Computing

• Step 1 – Load the input video
• Step 2 – Select the targeted object
• Step 3 – Initialize the parameters of Particle Swarm Optimization (PSO) Algorithm
• Step 4 – Compute and asses the Fitness function by Global best and Personal best (every particle) values
• Step 5 – When the Global best value is greater than pre-defined Threshold value, update speed and location of every particle
• Step 6 – Repeat the process until the stopping condition satisfied

How to Design the Best Soft Computing Model? 

• Extraction of appropriate datasets from performance sources
• Implementation of performance tools

In brief, extraction of the appropriate datasets indulges with several sources such as adaptive, huge multi-relational aspects. In addition to this, improve the prevailing performance tools in accordance with the imprecise parameters and command lines.

Performance Analysis of Soft Computing

Next, we can see the performance evaluation parameters for analysis modeled system efficiency to implement soft computing project ideas. Since it is the most important process in the development phase for assessing experimental results. For analysis purposes, we need to choose suitable performance parameters. Our developers are great at selecting suitable parameters based on project objectives. Here, we have given you some common parameters that are largely used in the evaluation of soft computing techniques. Further, we also assist you in other performance parameters to enhance the system efficiency. 

• Motivation
  • Directed to discover new solution for solving problem
• Determination
  • Specify the objective / aim of the technique
• Method
  • Automated sequential procedure to attain the targeted objective
• Where to implement
  • Need of selected parameter to address the application area where the technique going to be function
• Technique
  • Effort taken by input to achieve targeted objective (i.e., problem solving)
• Merging
  • Support various entities while attaining targeted objective / aim

Performance Metrics for Soft Computing

For better understanding, here we have categorized the parameters into three classifications such as probability, threshold, and rank parameters. Let’s see them in detail

• Probability Parameters
  • Compute deviation among true and predicted value
  • Used for regression related issue and classifier reliability evaluation
  • Include Root Mean Squared Error and Mean Absolute Error for matching result with threshold value
• Threshold Parameters
  • Compute closeness of prediction value to true value
  • Check whether the prediction value is below / above threshold value
  • Sensitive to F-measure, Thresholds, Kappa Statistic, Accuracy, etc.
• Rank Parameters
  • Compute ranking of positive instances over negative instances
  • Comprise area under PR curve / AUPRC and ROC curves / AUC for data retrieval
  • Identify the overall system performance over all applicable thresholds

Overall, we are here to support you in all the phases of soft computing project development. We assure you that we provide you end-to-end service starting from project topic selection to dissertation/thesis submission. Further, we also provide you with the project running video, execution procedure, project screenshots, software download links, and installation guidelines while delivering the project. We ensure you that meet your research and project expectations by all means like uniqueness, advance, quality, plagiarism-free, etc. So, connect with us to craft novel soft computing project ideas in your research career.