Evaluating Deep Learning Models for Autism Detection in Children Using Facial Images Udita J. Monani, Ritu Maity, Prasant Kumar Pattnaik, Kalaiarasi Sonai Muthu Anbananthen, Saravanan Muthaiyah, Mangal Sain Journal of Human Earth and Future, 2026 This study develops and evaluates a comprehensive deep-learning framework for early detection of Autism Spectrum Disorder (ASD) through facial image analysis. Five state-of-the-art convolutional neural network (CNN) architectures, VGG16, VGG19, ResNet50, InceptionV3, and MobileNet, were systematically assessed using a balanced dataset of 5,000 images (2,500 ASD, 2,500 non-ASD). Transfer learning and data augmentation enhanced model generalization. VGG19 achieved the highest overall accuracy (77.89%) and F1-score (0.7962), ResNet50 attained the best precision (82.53%), and InceptionV3 produced the highest recall (99.67%), indicating strong screening potential. The findings confirm that deep CNNs can capture subtle facial morphological cues linked to ASD, supporting their feasibility as non-invasive diagnostic tools. This work provides a benchmark for future multimodal, explainable, and clinically validated AI systems for autism detection.
Detection of Machining Error Using Intelligent Hybrid Machine Learning Technique Responsible AI Principles and Practices, 2026
Autonomous Vehicle Utilizing Deep Learning Methods Sresthasa Mohanty, Ritu Maity, Satya Ranjan Pattanaik 2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology IC Sit 2024, 2024 In today’s fast-paced world, individuals are constantly seeking ways to optimize their time and safety, especially during commutes and travel. Time management, traffic congestion, and safety are critical aspects of driving that have driven researchers to develop autonomous vehicles. A critical challenge in developing autonomous vehicles is achieving reliable lateral motion control. This paper addresses this issue by leveraging the deep learning techniques, and specifically explores the benefits of the NVIDIA Udacity car simulator. Our approach involves training a convolutional neural network (CNN) model to simulate the human driving behavior by utilizing a dataset of images and corresponding steering angles recorded during manual driving sessions in the simulator. It also emphasizes the importance of preprocessing the visual data to enhance the accuracy of the steering predictions. Our model is capable of managing complex driving scenarios within a simulated environment without the risks associated with real-world testing. A Mean Squared Error of 0.0123422 and an R-squared value of 0.8587687 suggest that our model is highly effective at navigating the car across different unfamiliar tracks.
A hybrid reinforcement learning and PSO approach for route discovery of flying robots Ritu Maity, Ruby Mishra, Prasant Kumar Pattnaik, Nguyen Thi Dieu Linh Risk Detection and Cyber Security for the Success of Contemporary Computing, 2023 Route discovery for flying robots is one of the major concerns while developing an autonomous aerial vehicle. Once a path planning algorithm is built and the flying robot reaches the destination point from the target point successfully, it is again important for the flying robot to come back to its original position and that is done through route discovery algorithms. Reinforcement learning is one of the popular machine learning methods in which the flying robot has to interact with the environment and learn by exploring the possibilities and maximum reward point method, without the requirement of a large amount of prior training data. Particle swarm optimization is an artificial intelligence inspired algorithm which finds optimal solution in a multi-dimensional space. This chapter has discussed a random exploration reinforcement learning approach combined with PSO algorithm that has been used to discover the optimum path for a flying robot to return from the destination point to the target point after it had traversed its best path from an already defined swarm intelligence technique. PSO+Reinforcement Learning (RL-PSO) is an optimization technique that combines the global search capability of PSO with the exploitation and exploration strategy of RL. Here higher reward points were assigned to the already defined best path obtained from the path planning technique, so that while returning from the destination point it will try to find the route with the highest reward point. With several iterations, it will optimize and find the best route for backpropagation. The algorithm is built using a python environment and the convergence result with the number of iterations has been validated.
Detection of damaged inserts of cutting tools using deep learning Ritu Maity Sustainable Science and Intelligent Technologies for Societal Development, 2023 Industrial manufacturing has become increasingly mechanized in recent years. The manufacturing productivity and costs of products are significantly impacted by machine cutting tools, which are a crucial component of industrial production. Tool breakage frequently happens suddenly and without warning in a practical manufacturing process, leading to an unusually unbalanced ratio of tool breakage samples to normal samples. Considering the need of current scenario of development of smart system in production units, the authors have proposed a deep learning-based model for prediction of damaged inserts which can give accurate results as compared to traditional techniques and manual inspection methods. The real time data of damaged tool and undamaged tools were collected, and the model training was done to predict defective inserts with high accuracy.
CRITIQUE OF DESIGN CHALLENGE OF FLYING ROBOTS Ritu Maity, Ruby Mishra, Prasant Kumar Pattnaik Biomedical Engineering Applications Basis and Communications, 2022 Flying robots popularly known as drones or UAVs are emerging technologies of the current era. A significant amount of research work has been undertaken in this area in the last few years. Considering the current scenario where aerial vehicles are taking a major part of the market it is important to have an effective and robust design of flying robots. This paper aims to examine the categories of flying robots based on the features that include a range from petite to large and its body structure, wing designs, tail design, propulsion system, and gripper mechanisms along with the associated materials and manufacturing techniques. Again the work is intended to review the respective challenges faced by each category. Mostly the challenges faced by flying robots are design challenges, material selection, and fabrication challenges which are discussed in the paper. In this paper, we have summarized various designs of flying robots developed to date as well as we have focused on major features to be taken care of while designing flying robots. This paper has tried to focus on different design aspects and challenges faced by flying robots so that further research can be carried out to develop effective flying robots in the future.
Detection of Machining Error Using Intelligent Hybrid Machine Learning Technique R Maity Responsible AI: Principles and Practices, 105-118 , 2026 2026
Secure and seamless design of smart door unlock system with raspberry pi app R Maity, R Mishra, R Soren, A Pandey, SK Nayak, BK Nanda AIP Conference Proceedings 3365 (1), 030066 , 2026 2026
Evaluating Deep Learning Models for Autism Detection in Children Using Facial Images M J. Monani, U., Maity, R., Pattnaik, P. K., Anbananthen, K. S. M ... Journal of Human, earth and future 7, 48-60 , 2026 2026
Analysis of path finding techniques for flying robots through intelligent decision-making algorithms in quantum inspired computing environment R Maity, R Mishra, PK Pattnaik Wireless Personal Communications 135 (3), 1561-1580 , 2024 2024 Citations: 4
Intelligent door unlock system using AI MediaPipe C Dutta, R Maity International Journal of Data Informatics and Intelligent Computing 3 (1), 36-43 , 2024 2024 Citations: 8
Autonomous Vehicle Utilizing Deep Learning Methods RMSRP S. Mohanty 2024 International Conference on Intelligent Computing and Sustainable … , 2024 2024
Selection of sustainable material for the construction of UAV aerodynamic wing using MCDM technique R Maity, R Mishra, PK Pattnaik, A Pandey Materials today: proceedings , 2023 2023 Citations: 21
A Hybrid Reinforcement Learning and PSO Approach R Maity, R Mishra, PK Pattnaik, NTD Linh Risk Detection and Cyber Security for the Success of Contemporary Computing, 23 , 2023 2023
BAT inspired regression model for prediction of power loss in solar pane R Maity Journal of Artificial Intelligence and Systems 5, 125-138 , 2023 2023 Citations: 1
Detection of Damaged Inserts of Cutting Tools Using Deep Learning R Maity Sustainable Science and Intelligent Technologies for Societal Development … , 2023 2023 Citations: 1
Flying robot path planning techniques and its trends R Maity, R Mishra, PK Pattnaik Materials Today: Proceedings 80, 2187-2192 , 2023 2023 Citations: 16
Critique of design challenge of flying robots R Maity, R Mishra, PK Pattnaik Biomedical Engineering: Applications, Basis and Communications 34 (06), 2230002 , 2022 2022 Citations: 3
Automatic face detection attendance system R Maity 2022
Design and Analysis of Hybrid Fixed‐Wing Type Flying Robot R Maity, R Mishra, PK Patnaik, M Sain Wireless Communications and Mobile Computing 2022 (1), 3978898 , 2022 2022 Citations: 8
A review of flying robot applications in healthcare R Maity, R Mishra, PK Pattnaik Smart Healthcare Analytics: State of the Art, 103-111 , 2021 2021 Citations: 17
An approach for detection of dust on solar panels using CNN from RGB dust image to predict power loss R Maity, M Shamaun Alam, A Pati Cognitive Computing in Human Cognition: Perspectives and Applications, 41-48 , 2020 2020 Citations: 31
MOST CITED SCHOLAR PUBLICATIONS
An approach for detection of dust on solar panels using CNN from RGB dust image to predict power loss R Maity, M Shamaun Alam, A Pati Cognitive Computing in Human Cognition: Perspectives and Applications, 41-48 , 2020 2020 Citations: 31
Selection of sustainable material for the construction of UAV aerodynamic wing using MCDM technique R Maity, R Mishra, PK Pattnaik, A Pandey Materials today: proceedings , 2023 2023 Citations: 21
A review of flying robot applications in healthcare R Maity, R Mishra, PK Pattnaik Smart Healthcare Analytics: State of the Art, 103-111 , 2021 2021 Citations: 17
Flying robot path planning techniques and its trends R Maity, R Mishra, PK Pattnaik Materials Today: Proceedings 80, 2187-2192 , 2023 2023 Citations: 16
Intelligent door unlock system using AI MediaPipe C Dutta, R Maity International Journal of Data Informatics and Intelligent Computing 3 (1), 36-43 , 2024 2024 Citations: 8
Design and Analysis of Hybrid Fixed‐Wing Type Flying Robot R Maity, R Mishra, PK Patnaik, M Sain Wireless Communications and Mobile Computing 2022 (1), 3978898 , 2022 2022 Citations: 8
Analysis of path finding techniques for flying robots through intelligent decision-making algorithms in quantum inspired computing environment R Maity, R Mishra, PK Pattnaik Wireless Personal Communications 135 (3), 1561-1580 , 2024 2024 Citations: 4
Critique of design challenge of flying robots R Maity, R Mishra, PK Pattnaik Biomedical Engineering: Applications, Basis and Communications 34 (06), 2230002 , 2022 2022 Citations: 3
BAT inspired regression model for prediction of power loss in solar pane R Maity Journal of Artificial Intelligence and Systems 5, 125-138 , 2023 2023 Citations: 1
Detection of Damaged Inserts of Cutting Tools Using Deep Learning R Maity Sustainable Science and Intelligent Technologies for Societal Development … , 2023 2023 Citations: 1
Detection of Machining Error Using Intelligent Hybrid Machine Learning Technique R Maity Responsible AI: Principles and Practices, 105-118 , 2026 2026
Secure and seamless design of smart door unlock system with raspberry pi app R Maity, R Mishra, R Soren, A Pandey, SK Nayak, BK Nanda AIP Conference Proceedings 3365 (1), 030066 , 2026 2026
Evaluating Deep Learning Models for Autism Detection in Children Using Facial Images M J. Monani, U., Maity, R., Pattnaik, P. K., Anbananthen, K. S. M ... Journal of Human, earth and future 7, 48-60 , 2026 2026
Autonomous Vehicle Utilizing Deep Learning Methods RMSRP S. Mohanty 2024 International Conference on Intelligent Computing and Sustainable … , 2024 2024
A Hybrid Reinforcement Learning and PSO Approach R Maity, R Mishra, PK Pattnaik, NTD Linh Risk Detection and Cyber Security for the Success of Contemporary Computing, 23 , 2023 2023
Automatic face detection attendance system R Maity 2022
