International Journal of Artificial Intelligence and Soft Computing (5 papers in press)

Regular Issues

• Early Identification of Acute Liver Failure through Machine Learning Algorithms  
  by Preety Shoran, Esha Saxena, Meenakshi Yadav, Subash Harizan, Akhilendra Khare, Saket Thankur, Avneesh Vashistha 
  Abstract: The liver plays a vital role in metabolism. This organ in the human body is responsible for maintaining and preserving overall health and well-being. However, when it fails to function optimally, it can cause severe and significant health complications. Liver diseases are multifactorial conditions that can be challenging to diagnose and treat. Early detection of any disease is very beneficial for effective treatment and diagnosis of patients conditions. Machine learning algorithms create a great platform for analysing medical data that helps in improving disease detection procedures. This paper aims to get a better understanding of machine learning algorithms in the detection of liver disease. We will explore different machine-learning techniques for predicting liver disease detection. It uses various parameters as symptoms and calculates acute liver failure (ALF) based on the parameters and ALF decides whether the patient has a liver disease or not. Accuracy was calculated with various machine learning techniques, i.e., logistic regression classification, KNN classification, decision tree, random forest and support vector machine (SVM). Out of these, logistic regression was found to be most effective in identifying and predicting the outcome of the dataset compared to other algorithms. SVM has a higher cross-validation score but accuracy, precision and recall are very low thus, cannot select this model.
  Keywords: Acute Liver Failure; Machine Learning; Feature Extraction; Liver Disease.
  DOI: 10.1504/IJAISC.2026.10076041
   
  
• Analysing Augmented Objective Function Values in Nonlinear Programming Problems Computationally using an Improved Particle Swarm Optimisation  
  by Raju Prajapati, Jayantika Pal, Om Prakash Dubey 
  Abstract: A mixed constrained Nonlinear Programming Problem (NLPP) contains equality as well as inequality constraints. Solution of the same is the goal of this paper. We use penalty methods for this purpose. A version of penalty method is a quadratic penalty method, which is effective on mixed constrained NLPPs. The quadratic penalty method is used for the general constrained NLPP for converting it to an unconstrained NLPP. An improved version of PSO is applied to solve the converted unconstrained NLPP. We consider the improved PSO with constant inertia weight and constriction factor with parameters/values as suggested in literature. Further, we also use a single random number and velocity clamping conditions. The augmented objective function values and objective function values are reported in limited number of iterations. The paper also certifies that on increasing the penalty constants, the computational objective values decrease significantly.
  Keywords: Penalty method; nonlinear programming problems; particle swarm optimization; mixed-constrained optimisation.
  DOI: 10.1504/IJAISC.2026.10076711
   
  
• A Comparative Study among Fuzzy C-Means Variants for Skin Lesion Segmentation  
  by Suman Bera, Rakesh Mahata, Agnish Ghosh, Bidisha Pal, Krishna Gopal Dhal 
  Abstract: Segmentation of skin lesions from dermoscopic images is crucial for the early diagnosis and prognosis of many skin disorders. Fuzzy C-Means (FCM) is a widely employed partitional clustering method that has been effectively applied for image segmentation. However, FCM exhibits many limitations, including longer computational time, estimation of cluster numbers, bad convergence, local optima trapping issues, and susceptibility to noise. Therefore, researchers developed some improved variants of FCM in the image segmentation field to overcome the said issues. This study employed seven popular improved FCM variants in the skin lesion image segmentation field and performed a rigorous comparative study among them. The segmentation results of the tested FCM variants have been evaluated visually and in terms of ground truth image-based quality metrics.
  Keywords: FCM; Lesion Segmentation; Skin Cancer; Clustering; Image Segmentation.
  DOI: 10.1504/IJAISC.2026.10077030
   
  
• Generative AI as Agent: Perceptual Analysis of Intrinsic and Extrinsic Factors from Agency and Chaos Theoretical Perspectives  
  by Alan D. Smith, Anna Abdulmanova 
  Abstract: Undoubtedly, the role of data analytics in understanding consumer behavior and providing quality interaction and experience has helped ignite the exponential growth of generative AI in society. This exploratory study of AI as an agent in decision-making processes was designed to examine which intrinsic and extrinsic motivational factors affect the adoption and implementation of AI technology. A sample of well-educated adults from Western PA and Northeastern OH was used to test the degree to which consumers accepted generative AI in their daily routines and decision-making activities. While many consumers are open to the use of AI and are willing to delegate some responsibilities to the technology, the empirical analysis showed significant concerns about privacy, trust, and ethical dilemmas when using AI for personal and work-related activities.
  Keywords: Agency Theory; big-data analytics; Chaos Theory; deep learning; ethical dilemma; generative AI; investment portfolios; hedonic gratification; human creativity and intuition; machine learning; omnichan.
  DOI: 10.1504/IJAISC.2026.10077171
   
  
• Application Based Integration of Artificial Intelligence and Block chain through Bibliometric-Content Analysis  
  by Diwakar Chaudhary, MOHIT ., Sapna Sugandha, Khadilkar Sujay Madhukar, Shubhash Kumar Verma, Rashi Baliyan 
  Abstract: Industry 4.0 (IR 4.0) is accelerating digital transformation, with rapid advancements in artificial intelligence (AI) and blockchain technologies fostering their convergence. Although prior studies have examined the adoption and technical integration of AI and blockchain, there remains limited consensus regarding the concrete business benefits arising from their combined application. Addressing this gap, the present study investigates the applications and advantages of blockchain-integrated AI systems across diverse business sectors. Using bibliometric analysis, the study systematically examines existing literature to identify influential publications, citation patterns, and key intellectual structures shaping this research domain. The analysis highlights dominant themes, emerging research characteristics, and underexplored areas within the intersection of AI, blockchain, and business. The findings demonstrate that AI-blockchain integration represents a significant and growing global phenomenon with substantial potential to enhance transparency, efficiency, and decision-making in business operations. The study underscores the need for further empirical research to address unresolved issues and guide future investigations in this evolving field.
  Keywords: Artificial Intelligence; Block Chain; Industrial Revolution 4.0.
  DOI: 10.1504/IJAISC.2026.10077280