Genetic Algorithm-Based Search Space Exploration to Generate Best Convolutional Neural Network Dhananjay Bisen, Praneet Saurabh, Mayank Thakur, Gyanendra Chaubey, Upendra Singh, Aditya Dubey IEEE Access, 2025 Despite the wide variety of applications and use cases that can be solved with the help of machine learning algorithms, researchers have yet to develop a general artificial intelligence solution that can solve different problems. A single machine learning model can provide at least or above-average human-level performance in multiple domains without re-training for each domain separately. Currently, machine learning solutions train multiple models for each domain and combine their outputs to generate high-performance in multi-domain results. While this multi-model approach is achievable, designing and training each model and fine-tuning hyperparameters is complex and time-consuming. However, automated machine learning approaches provide an algorithmic and probabilistic approach to training models with the best-performing hyperparameters. This paper, after taking inspiration from gene theory of adaptation, presents a Novel Inverse Shuffle crossover using the Genetic algorithm method that generates high-performance convolutional neural network architectures for search space exploration (GA-NISC). New integrations enable the proposed method to explore the hyperparameter search space more thoroughly before converging to the best-performing model architecture as compared to its peers. The experiments are performed on a benchmark German traffic signal classification dataset and the CIFAR-10 dataset. Experimental results revealed that the proposed method converged faster in contrast to other neural architectural search methods while reducing the training time and overall computations considerably. Besides, it also yield high fitness scores up to 92% and 98% respectively under the best generated architecture. Furthermore, a composite fitness metric was calculated by integrating Validation Accuracy, F1-Score, and Regularization into the cross-entropy loss metric to meaningfully improve model generalization, especially on imbalanced data.
Erythemato-Squamous Diseases Prediction and Interpretation Using Explainable AI Abhishek Singh Rathore, Siddhartha Kumar Arjaria, Manish Gupta, Gyanendra Chaubey, Amit Kumar Mishra, Vikram Rajpoot IETE Journal of Research, 2024 Erythemato-squamous diseases (ESD) diagnosis is a significant challenge in dermatology. It is divided into six categories. Artificial intelligence models have been applied to categorize these categories. Artificial intelligent models are black boxes in nature. The objective of this study is to unbox the black-box behavior and interpret the decision-making. Random Forest and XGBoost models are applied on a standard dataset with SHAP value to get interpretability and causability of decision. The Random Forest model had a classification accuracy of 98.21%. Integration of explainability increase the transparency of result and identify the root cause of the disease in the subject. A comprehensive quantitative study will help to adopt artificial intelligence in healthcare with ethical issues like transparency, causability, and interpretability of diagnosis.
Developing an Explainable Machine Learning-Based Thyroid Disease Prediction Model Siddhartha Kumar Arjaria, Abhishek Singh Rathore, Gyanendra Chaubey International Journal of Business Analytics, 2022 Healthcare and medicine are key areas where machine learning algorithms are widely used. The medical decision support systems thus created are accurate enough, however, they suffer from the lack of transparency in decision making and shows a black box behavior. However, transparency and trust are significant in the field of health and medicine and hence, a black box system is sub optimal in terms of widespread applicability and reach. Hence, the explainablility of the research make the system reliable and understandable, thereby enhancing its social acceptability. The presented work explores a thyroid disease diagnosis system. SHAP, a popular method based on coalition game theory is used for interpretability of results. The work explains the system behavior both locally and globally and shows how machine leaning can be used to ascertain the causality of the disease and support doctors to suggest the most effective treatment of the disease. The work not only demonstrates the results of machine learning algorithms but also explains related feature importance and model insights.
Hjorth Parameter based Seizure Diagnosis using Cluster Analysis Siddhartha Kumar Arjaria, Gyanendra Chaubey, Nishtha Shukla Journal of Physics Conference Series, 2021 The health-related issues have been increased with a wide range in few years. Hence the need for effective and advanced health care systems or aids isexpanding. New methodologies and instruments must be developed to aid the doctors inintelligent health caring of patients. Biomedical signals are a rich source of information, and it is not easy to understand by the normal human beings. To provide ease, extraction and analysis of biomedical signals can help get the correct information to everyone. The signals generated by the brain control the status of the mind and control the action of the whole body. Epilepsy is a disease by which around 50 million people are affected worldwide. Abnormal synchronisation of the neural activity with symptoms like convulsion is the phenomenon of epileptic seizures. An advanced seizure diagnosis system will help in the detection and diagnosis of epileptic seizures. In this paper, clustering algorithms are applied to Electroencephalogram (EEG) data to classify it in normal and epileptic seizures using the Hjorth parameters. After extracting the Hjorth parameters from EEG signals and k-means, basic sequential algorithmic scheme (BSAS), partitioning around medoids (PAM), fuzzy c-means (FCM), and Vally-Seeking clustering algorithms are applied to group it into normal and seizure. With the used dataset, the Vally Seeking clustering algorithm gives the best performance with an accuracy of about 87%.
Hand Gesture Identification System Using Convolutional Neural Networks Siddhartha Arjaria, Riya Sahu, Sejal Agrawal, Suyash Khare, Yashi Agarwal, Gyanendra Chaubey Proceedings 2021 1st IEEE International Conference on Artificial Intelligence and Machine Vision Aimv 2021, 2021 Recognition of hand movements is a key to conquering several difficulties and building warmth for human life. In an enormous number of applications, human actions and their significance are used in an array of applications to grasp the flexibility of machines. Sign language interpretation is one particular area of interest. Following paper describes a practical and interactive procedure for hand gesture detection by making use of a Convolutional Neural Network. The techniques are suitably graded into various stages during the process, such as the data acquisition, pre-processing, segmentation, extraction of features, and classification. The different algorithms that have done their task at each location are elaborated, along with their merits. Challenges and limitations faced during the process are discussed. Overall, it is hoped that the analysis might provide a detailed introduction into the sector of machine-driven gesture and signing acknowledgment and further facilitation of future research efforts in this sector. The proposed methodology has been tested over the 8700 images, and it classifies the images with an approximate accuracy of above 95%.
RECENT SCHOLAR PUBLICATIONS
T2I-BiasBench: A Multi-Metric Framework for Auditing Demographic and Cultural Bias in Text-to-Image Models N Jaiswal, S Arjaria, G Chaubey, A Kumar, A Singh, A Chaurasiya arXiv preprint arXiv:2604.12481 , 2026 2026
Genetic algorithm-based search space exploration to generate best Convolutional Neural Network D Bisen, P Saurabh, M Thakur, G Chaubey, U Singh, A Dubey IEEE Access , 2025 2025 Citations: 1
Chronic Kidney Disease Prediction and Interpretation Using Explainable AI AKM Siddhartha Kumar Arjaria, Abhishek Singh Rathore, Gyanendra Chaubey Communications in Computer and Information Science 1951, 29-44 , 2025 2025
RibCageImp: A Deep Learning Framework for 3D Ribcage Implant Generation G Chaubey, A Farooq, A Singh, D Mishra arXiv preprint arXiv:2411.09204 , 2024 2024 Citations: 1
Erythemato-Squamous Diseases Prediction and Interpretation Using Explainable AI AKMVR Abhishek Singh Rathore, Siddhartha Kumar Arjaria, Manish Gupta ... IETE Journal of Research 70 (1), 405–424 , 2024 2024 Citations: 15
Customer purchasing behavior prediction using machine learning classification techniques G Chaubey, PR Gavhane, D Bisen, SK Arjaria Journal of Ambient Intelligence and Humanized Computing 14 (12), 16133-16157 , 2023 2023 Citations: 93
Developing an explainable machine learning-based thyroid disease prediction model SK Arjaria, AS Rathore, G Chaubey International Journal of Business Analytics (IJBAN) 9 (3), 1-18 , 2022 2022 Citations: 21
Personality prediction through handwriting analysis using convolutional neural networks G Chaubey, SK Arjaria Proceedings of International Conference on Computational Intelligence: ICCI … , 2021 2021 Citations: 33
Hand Gesture Identification System Using Convolutional Neural Networks S Arjaria, R Sahu, S Agrawal, S Khare, Y Agarwal, G Chaubey 2021 International Conference on Artificial Intelligence and Machine Vision … , 2021 2021 Citations: 1
Hjorth Parameter based Seizure Diagnosis using Cluster Analysis SK Arjaria, G Chaubey, N Shukla Journal of Physics: Conference Series 1998 (01) , 2021 2021 Citations: 12
Thyroid disease prediction using machine learning approaches G Chaubey, D Bisen, S Arjaria, V Yadav National Academy Science Letters 44 (3), 233-238 , 2021 2021 Citations: 171
MOST CITED SCHOLAR PUBLICATIONS
Thyroid disease prediction using machine learning approaches G Chaubey, D Bisen, S Arjaria, V Yadav National Academy Science Letters 44 (3), 233-238 , 2021 2021 Citations: 171
Customer purchasing behavior prediction using machine learning classification techniques G Chaubey, PR Gavhane, D Bisen, SK Arjaria Journal of Ambient Intelligence and Humanized Computing 14 (12), 16133-16157 , 2023 2023 Citations: 93
Personality prediction through handwriting analysis using convolutional neural networks G Chaubey, SK Arjaria Proceedings of International Conference on Computational Intelligence: ICCI … , 2021 2021 Citations: 33
Developing an explainable machine learning-based thyroid disease prediction model SK Arjaria, AS Rathore, G Chaubey International Journal of Business Analytics (IJBAN) 9 (3), 1-18 , 2022 2022 Citations: 21
Erythemato-Squamous Diseases Prediction and Interpretation Using Explainable AI AKMVR Abhishek Singh Rathore, Siddhartha Kumar Arjaria, Manish Gupta ... IETE Journal of Research 70 (1), 405–424 , 2024 2024 Citations: 15
Hjorth Parameter based Seizure Diagnosis using Cluster Analysis SK Arjaria, G Chaubey, N Shukla Journal of Physics: Conference Series 1998 (01) , 2021 2021 Citations: 12
Genetic algorithm-based search space exploration to generate best Convolutional Neural Network D Bisen, P Saurabh, M Thakur, G Chaubey, U Singh, A Dubey IEEE Access , 2025 2025 Citations: 1
RibCageImp: A Deep Learning Framework for 3D Ribcage Implant Generation G Chaubey, A Farooq, A Singh, D Mishra arXiv preprint arXiv:2411.09204 , 2024 2024 Citations: 1
Hand Gesture Identification System Using Convolutional Neural Networks S Arjaria, R Sahu, S Agrawal, S Khare, Y Agarwal, G Chaubey 2021 International Conference on Artificial Intelligence and Machine Vision … , 2021 2021 Citations: 1
T2I-BiasBench: A Multi-Metric Framework for Auditing Demographic and Cultural Bias in Text-to-Image Models N Jaiswal, S Arjaria, G Chaubey, A Kumar, A Singh, A Chaurasiya arXiv preprint arXiv:2604.12481 , 2026 2026
Chronic Kidney Disease Prediction and Interpretation Using Explainable AI AKM Siddhartha Kumar Arjaria, Abhishek Singh Rathore, Gyanendra Chaubey Communications in Computer and Information Science 1951, 29-44 , 2025 2025
