Research & Consultancy Activity of CSE-AIML at Lords involves all stakeholders of the Education System. Our students & faculty have worked on various innovative projects and received patents for them. Every student and faculty is mandated to do Research on topics of their choice and publish papers in reputed journals. Our faculty members have published books that are used by students all across the World. In addition to publishing Books & Papers, our faculty and students have also worked on various Industrial Consultancy Assignments that have generated revenue for the Department.
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Patent GrantPatent PublicationConference PublicationBooks AuthoredJournal PublicationStudent Article PublicationStudent InnovationsStudent Conference Publication
Patents Grant
| Sr No | Name of Inventor(s) | Title of Invention | Application Number | National / International | Grant Date | Patent No. |
| 1 | Mrs.Sadia Kausar | IoT-Based Smart Robot for Air Conditioner Duct Cleaning | 481722-001 | National | 13/01/2026 | 481722-001 |
| 1 | Dr. Kamel Ali Khan Siddiqui | Portable Medication Dispenser with Automated Dosage Control | 452195-001 | National | 21/10/2025 | 452195-001 |
| 2 | Dr. Farheen Mohammed | Artificial Intelligence-Based Smart Toothbrush for Elderly People | 2024/05615 | International | 26/02/2025 | 2024/05615 |
Patents Publication
Conference Publication
| Sr No. | Name of Author | Title | Name of Conference | Month and Year of Conference | National/ International |
| 1 | Dr. Farheen Mohammed | AI-Based Information Transfer Scheduling in Cloud for Big Data Applications | International Journal of Scientific Research in Engineering and Management (IJSREM) | May, 2025 | International |
| 2 | Dr. Mohammed Tajuddin | AI in Education and Accessibility | Sustainable Practices and Innovations in Research and Engineering (INSPIRE-2025), | 4th to 5th April 2025 | International |
Book / Book Chapter Publication
Journal Publication
Student Publication
Student Innovations (2023-24)
| S.No | Roll Number | Name of the Students | Project Title | Small Description | Technology Involved | SDGs Addressed |
| 1 | 160920748004 | Mohammed Amaan Sarvar | DeepFake prediction with social media bot | Deepfake prediction involves detecting AI-generated synthetic media, such as images, audio, and videos. Advanced algorithms like GANs and Autoencoders create realistic but fake content, often amplified by social media bots. Predicting and preventing their spread is crucial to combating misinformation. | Generative Adversarial Networks (GANs) Convolutional Neural Networks (CNNs),Natural Language Processing (NLP) | SDG 9: Industry, Innovation, and Infrastructure SDG 16: Peace, Justice, and Strong Institutions SDG 17: Partnerships for the Goals |
| 160920748006 | Mohammed Khasim Ahmed Quadri | |||||
| 160920748056 | Syed Mohammed Manvi Quadri | |||||
| 2 | 160920748025 | Abdul Rahman | Machine Learning Based Patient Classification In Emergency Department | Machine learning (ML) is transforming healthcare by improving decision-making and patient care, especially in emergency departments. ML-based patient classification analyzes data like symptoms and vital signs to prioritize care, allocate resources efficiently, and reduce waiting times, ensuring timely treatment. | Supervised Learning Algorithms Natural Language Processing (NLP) | SDG 3: Good Health and Well-Being SDG 10: Reduced Inequalities |
| 160920748035 | Ismath Zehra | |||||
| 160920748 044 | Mir Ali Abbas | |||||
| 3 | 160920748 011 | Fareq Ali | Machine Learning-based Real-time Emotion Detection in Employees | ML-based real-time emotion detection analyzes facial expressions, voice, and physiological signals to assess employees’ emotions. It helps companies improve workplace well-being, address burnout, and boost engagement and productivity. | Convolutional Neural Networks (CNNs), Natural Language Processing (NLP) | SDG 8: Decent Work and Economic Growth SDG 9: Industry, Innovation, and Infrastructure SDG 16: Peace, Justice, and Strong Institutions |
| 160920748 018 | Mir Mustafa Ali | |||||
| 160920748 015 | Marwa Fatima | |||||
| 4 | 160920748 068 | Areeb Zaheer | Human Activity Recognition Through Ensemble Learning of Multiple Convolutional Neural Networks | Human Activity Recognition (HAR) identifies actions like walking or cycling using sensor data. Ensemble learning with multiple CNNs improves accuracy by combining model outputs, enhancing robustness even in complex environments. | Convolutional Neural Networks (CNNs),Data Preprocessing and Augmentation | SDG 9: Industry, Innovation, and Infrastructure SDG 11: Sustainable Cities and Communities |
| 160920748 078 | Mohammed Hafeezuddin | |||||
| 160920748061 | Anas Mohiuddin | |||||
| 5 | 160920748 100 | Mohammed Saif ur Rahman | Customer Churn Prediction in the Banking Industry Using Data Mining | Customer churn prediction in the banking industry refers to the process of identifying which customers are likely to leave (or “churn”) by using data mining techniques. By analyzing historical customer behavior data, such as transaction history, customer complaints, account activity, and engagement, banks can predict churn and take proactive measures to retain customers. Early identification of churn allows banks to target at-risk customers with personalized offers, improved services, or interventions to reduce attrition and maintain customer loyalty, leading to improved profitability and customer satisfaction. | Data Mining Techniques, Big Data Analytics, Machine Learning Algorithms | SDG 9: Industry, Innovation, and Infrastructure SDG 11: Sustainable Cities and Communities |
| 160920748 106 | Mohammed Safiuddin | |||||
| 160920748 312 | Abdul Azeez | |||||
| 6 | 160920748 063 | Mohammed Emaad Ali | Deep Learning Approach to Vision-based Human Activity Recognition | Human Activity Recognition (HAR) using computer vision refers to the process of recognizing and classifying human actions or behaviors (such as walking, running, sitting, etc.) by analyzing visual data, typically from video or images captured through cameras. A deep learning approach to HAR leverages powerful neural networks, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), to automatically learn spatial and temporal features from visual data. These networks are capable of identifying complex patterns in visual input, enabling real-time activity recognition. | Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) | SDG 9: Industry, Innovation, and Infrastructure SDG 16: Peace, Justice, and Strong Institutions SDG 17: Partnerships for the Goals |
| 160920748 067 | Mirza Munawar Ali Baig | |||||
| 160920748 098 | Mohammed Najeebuddin Quadri |
| Sr No. | Roll Number | Name of Author | Title | Name of Conference | Month and Year of Conference | National / International |
| 1 | 160923748034 | Mohammed Fazil | AI in Education and Accessibility | Sustainable Practices and Innovations in Research and Engineering (INSPIRE-2025), | 4th to 5th April 2025 | International |
| 2 | 160923748041 | Mohammed Feroz | ||||
| 3 | 160923748050 | Mohammed Yousuf Jameel | ||||
| 4 | 160923748010 | Abdul Mubeen |