Advancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamics Heyder Mhohamdi, Usama S. Altimari, Krunal Vaghela, V. Vivek, Sarbeswara Hota, Devendra Singh, Mahesh Manchanda, Shirin Shomurotova, Prakhar Tomar, Mohammad Mahtab Alam Scientific Reports, 2025 A combination of artificial intelligence (AI) and computational fluid dynamics was carried out to advance the modeling of adsorption separation processes. A comparative examination of three AI-based regression models including Gaussian Process Regression (GPR), Multi-layer Perceptron (MLP), and Polynomial Regression (PR) was carried out to predict chemical concentrations of solute in a dataset with two input variables (x and y) and one output feature (C in mol/m 3 ). Employing gradient-based hyperparameter optimization, the results reveal that MLP outperforms GPR and PR with a significantly higher R 2 score (MLP: 0.999, GPR: 0.966, PR: 0.980) and lower RMSE (MLP: 0.583, GPR: 3.022, PR: 2.370). Moreover, MLP demonstrates the lowest Average Absolute Relative Deviation (AARD%) at 2.564%, compared to GPR’s 18.733% and PR’s 11.327%. Five-fold cross-validation confirms MLP’s reliability (R² = 0.998 ± 0.001, RMSE = 0.590 ± 0.015). These findings underscore the practical utility of machine learning models, especially MLP, for accurate chemical concentration in environmental monitoring and process optimization with particular application for adsorption process.
Dehradun Vision 2025: Smart City Traffic Optimization Using YOLOv11, U-Net, and Deep SORT Sumit Saklani, Mahesh Manchanda 2025 International Conference on Networks and Cryptology Netcrypt 2025, 2025 This research presents an AI-powered traffic management system developed to solve Dehradun's urban mobility issues in alignment with Dehradun Smart City Vision 2025. It implements YOLOv11 for vehicle detection, U-Net for lane segmentation, Deep SORT for multi object tracking, and IoT-based adaptive signal control. Unlike fixed-timer systems, it adjusts signals in real-time considering traffic density, pedestrian movement, and other environmental factors. Using 3000 images from eight vehicle classes, the system outperformed YOLOv5, YOLOv7, and YOLOv8 with a 96.4% detection accuracy at 125 FPS, which also decreased waiting time by 38 %. Detection accuracy nighttime for pedestrians boosted safety in low-visibility conditions to 92.5 %. The system also reduced <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CO}_{2}$</tex> emissions by 35 % while maintaining 15 ms latency with 5 G transmission, achieving real-time decision making, reinforcing sustainability goals. The system outperformed other models in high-density traffic regions, and served as a proof of concept to show deep learning, IoT, and real-time optimization can improve urban mobility, congestion, and smart sustainable city planning.
An Overview of Automated Breast Cancer Diagnosis based on Mammographic Images using Deep Learning Methods Gunjan Mehra, Mahesh Manchanda, Neelam Singh Proceedings of the 4th International Conference on Intelligent Computing Information and Control Systems Icoiics 2025, 2025 Breast cancer remains one of the leading causes of mortality among women worldwide, making early and reliable diagnosis a critical healthcare priority. With the increasing availability of mammographic imaging and advances in artificial intelligence, deep learning has emerged as a transformative approach for computer-aided diagnosis. This paper presents a comprehensive review of automated breast cancer detection methods using mammographic images, focusing specifically on deep learning-based techniques. Unlike original research studies, this review compiles and synthesizes findings from recent works (2018-2025), highlighting commonly used datasets, preprocessing strategies, model architectures, and evaluation metrics. Comparative insights are provided to understand why certain models outperform others under specific conditions, along with a discussion of research gaps such as dataset bias, interpretability, federated learning, and domain adaptation. The novelty of this work lies in providing an organized overview and critical synthesis of existing studies rather than proposing a new model or experiment. This survey aims to guide future research toward addressing limitations in current methodologies and fostering clinically reliable breast cancer diagnostic systems.
IOT-Enabled Smart Home Automation with YOLOv5 Human Detection Raman Sharma, Mahesh Manchanda, Sumit Saklani 2025 3rd International Conference on Communication Security and Artificial Intelligence Iccsai 2025, 2025 This paper demonstrates an IoT connected Smart Home automation using Realtime Human Detection and Personalized Environment Adaptation based on YOLOv5 model. In this study, we proposed a deep learning and IoT integration system that would provide convenience, privacy, and energy efficiency in homes of today. The device controls lighting and HVAC systems in real time by detecting human presence, thus saving with energy and making user feel comfort. Experiments show that it can reach 95% accuracy in detection on any possible real-world data, respond in real-time with low latency and save a non-trivial amount of energy. In addition, the system achieves this by drastically lowering data transmission to the outside and keeping most of it local. This model scales very well with performance in multi-user environments verified by scalability tests. The present work underscores the transformative power of AI-enabled IoT systems in the architectural automation of intelligent homes with user-driven solutions.
YOLOv5 and LSTM-Based Driver Drowsiness Detection with Emergency Protocols for Smart Vehicles Raman Sharma, Mahesh Manchanda, Sumit Saklani 2025 International Conference on Networks and Cryptology Netcrypt 2025, 2025 An intelligent driver monitoring and hazard detection system that utilizes YOLOv5, a deep learning object detection model, and Long Short-Term Memory (LSTM) networks has been developed to escalate the safety of vehicles. The system uses YOLOv5 for facial recognition and monitoring of the environment around the vehicle, which includes detection of potholes, pedestrians, and erratic motor vehicle movements. Moreover, LSTM analyses the movement patterns of the driver's mouth and eyes to track possible signs of drowsiness or other medical conditions. Once a danger is detected, either from the environment or posed physiologically, the system puts in place measures that include alerting other vehicles and notifying emergency personnel through IoT channels. The system utilizes a blend of standard and customized datasets for training, taking into consideration different weather conditions to improve robustness. Evaluation tests of the model indicate it has achieved a validation accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 2 \%}$</tex> in estimating with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 4 \%}$</tex> accuracy during daytime hours, while over 85% of the time in foggy or rainy conditions. The system also reduces the false positive rate to 6.4% while improving emergency response accuracy to 96% which outperforms predecessor systems in both reliability and responsiveness. This two-fold approach system provides an enhanced perspective toward safety in smart vehicles. Expanding this work in the future may include the integration of biometrics devices, predictive analytics, and application into more complex autonomous vehicular systems which may be useful for thorough testing and evaluation.
Information Technology and Panch Parivartan: Framework for Sustainable Bharat 2047 Information Technology and Panch Parivartan Framework for Sustainable Bharat 2047, 2025
A Comparative Study of Simulated Annealing and Ant Colony Optimization for Optimizing MRI-Based Alzheimer's Disease Classification International Journal of Intelligent Systems and Applications in Engineering, 2024
DEVELOPMENT OF PRECISION MEDICINE FOR HEPATIC CANCER EVOLVING MOLECULAR PROFILING TECHNIQUES THROUGH ARTIFICIAL INTELLIGENCE Journal of the Balkan Tribological Association, 2024
Identifying Biomarkers from Medical Images Using Machine Learning Techniques International Journal of Intelligent Systems and Applications in Engineering, 2023
Real-Time Analysis of Wearable Sensor Data Using IoT and Machine Learning in Healthcare International Journal of Intelligent Systems and Applications in Engineering, 2023
Prediction of Turkey Forest Fire using Random Forest Regressor Umang Garg, Vineet Kukreti, Rahul Singh Pundir, Mahesh Manchanda, Neha Gupta International Conference on Innovative Data Communication Technologies and Application Icidca 2023 Proceedings, 2023
Implementation and Visualization of Path Finding Algorithms Chandradeep Bhatt, Ritabh Sharma, Rahul Chauhan, Ashish Vishvakarma, Mahesh Manchanda, Sanjay Sharma Proceedings of the 5th International Conference on Inventive Research in Computing Applications Icirca 2023, 2023
New insights into sulfur dioxide absorption in deep eutectic solvents C Hu, FMA Altalbawy, K Vaghela, V Vivek, S Hota, D Singh, ... Microchemical Journal, 117312 , 2026 2026
Comparative analysis of real and AI-generated sentiment data: exploring linguistic and contextual differences in financial texts PK Kaushik, RK Bisht, M Manchanda, AK Sahoo Journal of Ambient Intelligence and Humanized Computing, 1-15 , 2026 2026
Advanced hybrid evaluation of water treatment using porous materials for adsorption separation via machine learning and mechanistic models P Zhang, US Altimari, K Vaghela, V Vivek, S Hota, D Singh, M Manchanda, ... Case Studies in Thermal Engineering, 107561 , 2025 2025
An Overview of Automated Breast Cancer Diagnosis Based on Mammographic Images Using Deep Learning Methods G Mehra, M Manchanda, N Singh 2025 International Conference on Intelligent Computing, Information and … , 2025 2025
Revolutionizing Crop Yield Prediction with Cutting-Edge Machine Learning and Big Data Techniques P Bagla, P Bhatt, R Sharma, G Chhabra, M Manchanda, AK Mishra 2025 2nd Global AI Summit-International Conference on Artificial … , 2025 2025
Advancing Breast Cancer Detection Through ConvNeXt-Tiny and Attention-Based Deep Learning G Mehra, M Manchanda, M Shuaib, A Joshi, A Juyal, M Diwakar 2025 2nd Global AI Summit-International Conference on Artificial … , 2025 2025
Hybrid Approaches for Predicting Bankruptcy: Integration of Financial Ratios and ANOVA in Machine Learning R Rawat, M Manchanda, S Kumar 2025 International Conference on Artificial intelligence and Emerging … , 2025 2025
Advancing computational evaluation of adsorption via porous materials by artificial intelligence and computational fluid dynamics H Mhohamdi, US Altimari, K Vaghela, V Vivek, S Hota, D Singh, ... Scientific Reports 15 (1), 29691 , 2025 2025 Citations: 4
Introduction to Machine Learning and Image Processing for Disease Detection PK Kaushik, M Manchanda, RK Bisht Machine Learning for Disease Detection, Prediction, and Diagnosis … , 2025 2025
Dehradun Vision 2025: Smart City Traffic Optimization Using YOLOv11, U-Net, and Deep SORT S Saklani, M Manchanda 2025 International Conference on Networks and Cryptology (NETCRYPT), 1036-1041 , 2025 2025 Citations: 4
YOLOv5 and LSTM-Based Driver Drowsiness Detection with Emergency Protocols for Smart Vehicles R Sharma, M Manchanda, S Saklani 2025 International Conference on Networks and Cryptology (NETCRYPT), 1042-1046 , 2025 2025 Citations: 3
Comparing Random Forest and XGBoost for Sentiment Classification of Student Social Media Posts: A Case Study on Pre-Board Exam Stress S Saklani, M Manchanda, G Bisht, K Thapa 2025 Global Conference in Emerging Technology (GINOTECH), 1-6 , 2025 2025 Citations: 5
IOT-Enabled Smart Home Automation with YOLOv5 Human Detection R Sharma, M Manchanda, S Saklani 2025 3rd International Conference on Communication, Security, and Artificial … , 2025 2025
Historical Data Based Sentiment-Driven Stock Market Prediction Using a Hybrid BERT-LSTM Model S Yadav, S Keswani, AS Sengar, M Manchanda, MA Mohammed, ... 2025 International Conference on Next Generation Information System … , 2025 2025 Citations: 1
Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration S Saklani, M Manchanda, R Sharma, D Singh 2025 First International Conference on Advances in Computer Science … , 2025 2025 Citations: 9
Netflix stock market price prediction U Sharma, A Kumar, R Kumar, M Manchanda, S Hooda AIP Conference Proceedings 3224 (1), 020061 , 2025 2025
Harnessing AI for English Language Learning through Personalized Methods and Adaptive Technologies MV Kumar, N Prakash, M Manchanda, R Changala 2025 International Conference on Intelligent Systems and Computational … , 2025 2025 Citations: 4
Enhancing Writing Skills with AI: Personalized Feedback Mechanisms for English Learners S Dasam, P Goriparthi, M Manchanda, PK Nowbattula, R Subhashini 2024 International Conference on Artificial Intelligence and Quantum … , 2024 2024 Citations: 6
Applying Artificial Intelligence in English Language Learning for Customized Education VM Jayakumar, R Rajakumari, PR Alapati, M Manchanda, M Vimochana, ... 2024 International Conference on Artificial Intelligence and Quantum … , 2024 2024 Citations: 2
Fracture Detection in X-Ray Images Using Whale Optimization Algorithm-Enhanced Bi-Directional LSTM Networks RG Tiwari, M Manchanda, AK Agarwal 2024 13th International Conference on System Modeling & Advancement in … , 2024 2024 Citations: 1
MOST CITED SCHOLAR PUBLICATIONS
Financial fraud detection using naive bayes algorithm in highly imbalance data set A Gupta, MC Lohani, M Manchanda Journal of Discrete Mathematical Sciences and Cryptography 24 (5), 1559-1572 , 2021 2021 Citations: 92
CROP RECOMMENDATION SYSTEM USING MACHINE LEARNING ALGORITHMS MM Vaibhav Sharma, Sweety Bharti, Sarvesh Rustagi JOURNAL OF EAST CHINA UNIVERSITY OF SCIENCE AND TECHNOLOGY 65 (3), 155-165 , 2022 2022 Citations: 71
Bioinformatics and biological data mining RKK Aditya Harbola, Deepti Negi, Mahesh Manchanda Bioinformatics Methods and applications, 457-471 , 2022 2022 Citations: 21
Molecular insights into a mechanism of resveratrol action using hybrid computational docking/CoMFA and machine learning approach A Pande, M Manchanda, HR Bhat, PS Bairy, N Kumar, P Gahtori Journal of Biomolecular Structure and Dynamics 40 (18), 8286-8300 , 2022 2022 Citations: 16
A CNN Method Based Predictive Model for Tomato Leaf Disease Prediction J Agarwal, S Gupta, N Sharma, M Manchanda 2023 3rd International Conference on Technological Advancements in … , 2023 2023 Citations: 14
Enhancing resume recommendation system through skill-based similarity using deep learning models RS Pundir, A Dhasmana, U Karakoti, A Sikder, S Sharma, M Manchanda 2024 International Conference on Inventive Computation Technologies (ICICT … , 2024 2024 Citations: 11
Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration S Saklani, M Manchanda, R Sharma, D Singh 2025 First International Conference on Advances in Computer Science … , 2025 2025 Citations: 9
Real-time analysis of wearable sensor data using IoT and machine learning in healthcare H Keserwani, SV Kakade, SK Sharma, M Manchanda, GF Nama International Journal of Intelligent Systems and Applications in Engineering … , 2023 2023 Citations: 9
Efficient Diagnosis of Coffee Leaf Diseases Using Pre-Trained ResNet50 Neural Networks S Singla, M Manchanda, G Sunil 2024 International Conference on Information Science and Communications … , 2024 2024 Citations: 8
Automation and Computation: Proceedings of the International Conference on Automation and Computation,(AutoCom 2022), Dehradun, India S Vats, V Sharma, K Singh, A Gupta, D Bordoloi, N Garg CRC Press , 2023 2023 Citations: 8
An Extensive Review on Web Scraping Technique using Python R Chauhan, A Negi, M Manchanda 2023 Second International Conference on Augmented Intelligence and … , 2023 2023 Citations: 7
A Survey Paper on Precision Agriculture based Intelligent system for Plant Leaf Disease Identification Supriya, A Shukla, M Manchanda International Conference on Artificial Intelligence and Applications (ICAIA … , 2023 2023 Citations: 7
Enhancing Writing Skills with AI: Personalized Feedback Mechanisms for English Learners S Dasam, P Goriparthi, M Manchanda, PK Nowbattula, R Subhashini 2024 International Conference on Artificial Intelligence and Quantum … , 2024 2024 Citations: 6
An efficient secure predictive demand forecasting system using Ethereum virtual machine H Saraswat, M Manchanda, S Jasola IET Blockchain 4, 526-542 , 2024 2024 Citations: 6
A real time tomato crop disease prediction using deep learning M Manchanda, K Jadli, P Butola, V Sharma, D Bisht, K Purohit 2024 international conference on inventive computation technologies (ICICT … , 2024 2024 Citations: 6
Prediction of turkey forest fire using random forest regressor U Garg, V Kukreti, RS Pundir, M Manchanda, N Gupta 2023 International Conference on Innovative Data Communication Technologies … , 2023 2023 Citations: 6
Comparing Random Forest and XGBoost for Sentiment Classification of Student Social Media Posts: A Case Study on Pre-Board Exam Stress S Saklani, M Manchanda, G Bisht, K Thapa 2025 Global Conference in Emerging Technology (GINOTECH), 1-6 , 2025 2025 Citations: 5
AATAD: ESP8266 Based Home Automation System With Enhanced Security Using Voice Identification And Recognition Technology G Dangwal, P Matta, S Maurya, S Kukreti, M Manchanda 2023 6th International Conference on Contemporary Computing and Informatics … , 2023 2023 Citations: 5
Classification and prediction of Kashmiri apple plant by using deep learning techniques U Garg, K Jadli, RS Pundir, M Manchanda, N Gupta 2023 International Conference on Device Intelligence, Computing and … , 2023 2023 Citations: 5
Misinformation classification using LSTM and BERT model A Harbola, M Manchanda, D Negi 2023 International Conference on Innovative Data Communication Technologies … , 2023 2023 Citations: 5
