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International Bhurban Conference on Applied Sciences & technology, IBCAST

作者： Muhammad Owais Tariq Shafaat Ahmad Bazaz Hussain Ahmed Faculty of Engineering and Technology The Superior University Lahore Pakistan Department of Electrical and Computer Engineering Sir Syed CASE Institute of Technology Islamabad Pakistan Department of Sciences & Humanities Sir Syed CASE Institute of Technology Islamabad Pakistan

ISBN: (数字)9798331516680

ISBN: (纸本)9798331516697

The research described the design and analysis of a bistable actuator for MEMS devices using a curved beam structure. The investigation revolves around the optimization of length, width, and apex height of the microstructure to deflect it to the actuated position. The mechanical behavior was validated using Finite Element Analysis using Ansys Multiphysics with a deviation of 1.94%. The result shows that the beam maintains its second stable state without continuous application of the force which significantly reduces the power consumption.

关键词： Micromechanical devices Actuators Power demand Force Micromachining Stability analysis Finite element analysis Residual stresses Microstructure Optimization

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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A Comprehensive Survey of Techniques for Lung Cancer Diagnosis and Prediction

A Comprehensive Survey of Techniques for Lung Cancer Diagnos...

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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Danish Jamil syed Mehr Ali Shah Fatma Mahfoodh Mohammed Al-Jarwani Department of Software Engineering Sir Syed University of Engineering & Technology Karachi Pakistan College of Computing and Information Sciences University of Technology and Applied Sciences Musandam Sultanate of Oman

ISBN: (数字)9798331534271

ISBN: (纸本)9798331534288

Despite significant advancements, lung cancer remains a formidable global health challenge, necessitating effective diagnostic and prognostic methodologies. This survey paper examines current literature to identify and evaluate methods and models utilized for lung cancer diagnosis and prognosis, aiming to enhance early detection strategies for improved patient outcomes. The research addresses critical questions: What are the current methods and models employed for lung cancer diagnosis and prognosis, their strengths, limitations, and potential for future improvement? It investigates specific applications of deep learning architectures such as CNN, GoogleNet, VGG-16, U-Net, and machine learning algorithms like XGBoost, SVM, KNN, ANN, Random Forest, and hybrid models in lung cancer detection. Methodologically, a systematic review across key databases using relevant keywords was conducted to synthesize findings. The study highlights the strengths and limitations of existing approaches and identifies gaps in model interpretability, real-world validation, and integration of diverse techniques. Conclusions underscore the transformative potential of advanced computational methods in enhancing lung cancer diagnosis and prognosis, proposing avenues for future research to address current challenges and improve patient care.

关键词： Surveys Deep learning Machine learning algorithms Accuracy Computational modeling Transfer learning Lung cancer Medical services Prognostics and health management Systematic literature review

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KiAtt Fusion: Remote Sensing-Based Water Bodies Segmentation for Environmental Monitoring

KiAtt Fusion: Remote Sensing-Based Water Bodies Segmentation...

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Digital Futures and Transformative Technologies (ICoDT2), International Conference on

作者： Saad Sikander Hamood Ur Rehman syed Fakhr Abbas Naqvi Waqar Aslam Nazia Perwaiz Department of Electrical Engineering Sir Syed CASE Institute of Technology Islamabad Pakistan Department of Electrical Engineering and Computer Science Sir Syed CASE Institute of Technology Islamabad Pakistan School of Electrical Engineering and Computer Science National University of Sciences and Technology NUST Islamabad Pakistan

ISBN: (数字)9798350391862

ISBN: (纸本)9798350391879

Monitoring the environment and managing water bodies are crucial for preserving ecosystems and ensuring sustainable resource utilization. This study aims to propose a robust approach for segmenting water bodies by combining various complementary attributes extracted from deep learning techniques. By leveraging deep contextual features learned from attention regions and enhanced edge detection, our method significantly improves the accuracy of detecting water bodies in low-resolution satellite imagery. Utilizing S1 and S2 Sentinel imagery data and exploring multi-band features, we enhance key attributes through advanced UNet architectures—specifically, Ki-UNet and Attention UNet—and integrate complementary features for downstream tasks. Additionally, we assembled a local dataset from the Khyber Pakhtunkhwa (KPK) region of Pakistan, facilitating precise water body segmentation from satellite or aerial views. Our ensemble model achieved remarkable accuracy and Intersection over Union (IoU) scores, reaching up to 99.01% and 96.2%, respectively, surpassing state-of-the-art models. This research provides automated, accurate segmentation techniques essential for environmental management and resource assessment, offering a promising solution for delineating water bodies. github link : (https://***/SaadBaloch96/Dataset/blob/main/***)

关键词： Image segmentation Accuracy Satellites Image edge detection Feature extraction Satellite images Resource management Water resources Remote sensing Software development management

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Numerical simulation along with the experimental work for an underdamped oscillator using fourth order Runge-Kutta method. An undergraduate experiment

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Physics Education 2023年第6期58卷 065006-065006页

作者： Khan, Kamran-Ul-Haq Masroor, Suhaib Department of Physics University of Karachi Karachi Pakistan Department of Biomedical Engineering Sir Syed University of Engineering and Technology Karachi Pakistan

Experiments based on oscillatory motion are an essential part of the curricula for students of physics and engineering in their undergraduate studies, such as the determination of spring constant for a well-known classical oscillator i.e., spring-mass system. Moreover, it is important for students to understand the physics of damped oscillators because, in real-world scenarios, a system involving oscillations cannot be completely analysed without understanding conditions of damped oscillations i.e., underdamped, overdamped, and critical damped. In this work, we design a computational physics lab for the simulation of the underdamped oscillator using an Excel spreadsheet by employing 4th order Runge-Kutta method. Further, we construct a simple experimental setup to observe the damped oscillation and obtaindata for the underdamped system. Final analysis was based on comparison between the experimental data and the numerically estimated data. © 2023 IOP Publishing Ltd.

关键词： damped oscillations Excel spreadsheet Runge-Kutta (RK) method spring-mass system

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Improving Traffic Surveillance: Deep Learning Approach for Road Anomaly Detection in Videos

Improving Traffic Surveillance: Deep Learning Approach for R...

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Computing and Machine Intelligence (ICMI), International Conference on

作者： Sarfaraz Natha Muhammad Arif syed Shahryar Jamil Fareed Ahmed Jokhio Muslim Jameel syed Department of Software Engineering Sir Syed University of Engineering and Technology Karachi Pakistan Instituto Politécnico Nacional (IPN) Mexico MAJU Karachi Pakistan Department of Computer Systems Engineering Quaid-e-Awam University of Engineering and Technology Nawabshah Pakistan Department of Enterprise and Technology Atlantic Technological University Galway Ireland

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

In recent years, Closed-circuit Television (CCTV) cameras have been playing a vital role in the surveillance of both public and private areas. The primary objective of surveillance is to monitor human behavior and road conditions. In the real-world situation, detecting, and recognizing abnormal activities poses significant challenges due to the densely crowded environment and the complex nature of transportation systems. These factors make it difficult to automatically identify various anomalies that occur while traveling, leading to emergencies, and endangering human life and property. This study introduces an automatic detection framework for recognizing road anomalies such as accidents, fighting, car fires, and armed snatching (gunpoint) in road surveillance videos. After reviewing the literature, the review directs that convolutional neural networks (CNNs) are a specialized deep learning approach well suited for image and video analysis. The proposed methodology combines the pretrained CNN models with Data Augmentation (DA) techniques to fine-tune hyperparameters such as learning rate and momentum that enhance the model learning accuracy and performance for recognizing road anomalies. Furthermore, it introduced a rolling prediction algorithm to solve the flickering problem during testing and created a new road anomaly dataset (RAD) as a benchmark consisting of road surveillance videos and images. Our proposed model combined with the InceptionV3 pre-trained model achieved a best accuracy is $98.81\%$ for detection and classification as compared to other deep learning models.

关键词： Deep learning Accuracy Roads Surveillance Computational modeling Prediction algorithms Data augmentation

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A Comparative Study of the Performance of Real time databases and Big data Analytics Frameworks

A Comparative Study of the Performance of Real time database...

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International Multi-Topic ICT Conference (IMTIC)

作者： syed Muhammd Nabeel Mustafa Muhammad Umer Farooq syeda Sundus Zehra Jospeh Peter Terenece Noronha Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed University of Engineering and Technology Karachi Pakistan

The amelioration of information technology and the infiltration of it almost all the sectors of world have resulted in massive efflux of data. The rampant increase in the generation of data has introduced to a new term called, Big data. Big data is letting industry set the pace of future research and development. The need rose to further refine the data and get most out of it. Scientists and researchers performed analytic on the Big data to analyze it for patterns and behaviors hidden inside the data. Big data analytics aid in understanding the data through various tools, algorithms and frameworks. It allows stakeholders to make business plans and strategies according to the revelations from data. There are various tools and technologies available to perform analytics on the data. The most popular among scientists and researchers are real-time database and Big data analytics frameworks. In the paper, we evaluate the performance of the tools and technologies of Big data analytics. Mongodb and Firebase are real-time databases implemented in this study and Impala and Hive are the BDA frameworks. The parameter to perform the comparison is the query execution time. It is found out that Impala and Hive gave lesser execution time than Mongodb and Firebase. The query execution time of Impala on 50 mb dataset and 100 mb dataset was recorded to be 4 ms and 14 ms respectively. Hence, Big data Analytics Frameworks are better performing analytics tools for Big data Analytics.

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Hybrid Channel Attention Regression U-Net (ARU-Net): An Enhanced Architecture for Brain Tumour Segmentation in Magnetic Resonance Imaging

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IEEE Access 2025年 13卷 102278-102298页

作者： Siddiqui, Noman Ahmed Qadri, Muhammad Tahir Ali, Zain Anwar Akhter, Muhammad Ovais Alsanad, Ahmed Alhogail, Areej Abdullah Gumaei, Abdul H. Sir Syed University of Engineering and Technology Electronic Engineering Department Karachi75290 Pakistan Co. Kildare MaynoothW23 A3HY Ireland Bahria University Electrical Engineering Department Karachi73500 Pakistan King Saud University College of Computer and Information Science Riyadh11543 Saudi Arabia Prince Sattam bin Abdulaziz University College of Computer Engineering and Sciences Al-Kharj11942 Saudi Arabia

This research presents an accurate segmentation of brain tumours which is important in diagnosis and treatment planning of magnetic resource imaging (MRI) scans. Despite existing methodologies based on deep learning models, it remains difficult to distinguish between a tumour region and a normal brain tissue. To tackle this problem, a recent work proposed a new brain tumour segmentation technique on MRI, known as the Hybrid Attention Regression U-Net (ARU-Net) architecture. To enhance the effectiveness of segmentation, the proposed architecture utilises modern approaches including attention mechanisms, feature fusion processes, and regression-based output layer. It specifically uses Spatial and channel-wise attention mechanisms to capture important regions and inter channel dependencies. Spatial attention module in this architecture is based on improved Convolutional Block Attention Module which constrain attention on informative areas and suppress irrelevant or noisy ones. On the other hand, the channel-wise attention part, the Squeeze-and-Excitation (SE) blocks, recalibrates channel-wise feature responses for higher emphasis on salient features. The performance of proposed method has been evaluated comprehensively on the BraTS-2021 dataset where it shows enhanced accuracy over existing methods in detecting the tumour area. From the point of epoch curve, F1 score, the area under the curve, specificity, sensitivity, and accuracy in augmented and non-augmented datasets validated the stability of the proposed ARU-Net. This underlines its roles in segmentation of brain tumour, which if optimally developed holds the promise to significantly contribute for diagnosis and treatment planning. © 2013 IEEE.

关键词： Image segmentation

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Rain Predictive Model using Machine learning Techniques

Rain Predictive Model using Machine learning Techniques

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Multi-disciplinary Conference in Emerging Research Trends (IMCERT), International

作者： Muhammad Shahbaz Muneer syed Muhammad Nabeel Mustafa syeda Sundus Zehra Haniya Maqsood Dpertment of Computational Finance NED University of Engineering and Technology Karachi Pakistan Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed Uuniversity of Engineering and Technology Karachi Pakistan Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan

Climate is rapidly changing around the world. Over time, there have been significant changes in the weather. Rainfall is now erratic due to climate change. The frequency of extreme weather events like droughts and floods has increased due to climate change, necessitating the need for more precise and timely rainfall forecasts. For strategic reasons including agriculture, water resource management, and architectural design, rain forecasting is crucial. The naturally occurring non-stationary component in the rainfall time series impairs model performance for practical hydrologists and drought risk assessors. We present a rain predicting model based on machine learning to address the forecasting issue. In our work, we predict the possibility of rain the next day on the basis of last 10 years' data. The variables that were calculated during the experiments were humidity, pressure, evaporation, sunshine, rainfall, and so on. Random Forest gave the 90% accuracy with 0.904 Area under Curve, highest out of all the algorithms. The model's performance will significantly aid in the rain forecast.

关键词： Climate change Rain Machine learning algorithms Droughts Weather forecasting Predictive models Prediction algorithms

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Abnormalities Detection in Chest CT Scan Images using Federated and Deep Learning

Abnormalities Detection in Chest CT Scan Images using Federa...

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International Conference on Emerging Technologies, ICET

作者： Mashab Ali Javed syed Jawad Hussain Aqsa Yasmeen Amaad Khalil Muhammad Bilal Rafaqat Department of Electrical and Computer Engineering Sir Syed CASE Institute of Technology Islamabad Pakistan Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan School of ICT University of Tasmania Hobart Australia

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

Lung cancer, which accounts for an estimated 2.20 million reported cases and leading to 1.80 million fatalities in the year 2020, represents a significant global cause of mortality. Early detection during Computed Tomography (CT) chest scans can improve survival rate of patients. To address the growing use of CT scan, a robust automated model is required to eliminate tedious manual procedures and reduce variability in diagnosis. Moreover, there are challenges associated with medical research such as data privacy, collaboration, regulatory compliance, and data diversity. In this study, we propose two machine learning (ML) methods and subsequently conduct a comparative analysis. In the first approach, we presented a deep learning technique to achieve high image classification accuracy by using multi-layer CNN and two pre-trained models, namely ResNet50 and DenseNet201. In the second approach, we presented a Federated Learning (FL) technique, which entails the utilization of all three models for classifying abnormalities in chest CT scans. Our results illustrate that the FL-based approach can assist in the diagnosis of anomalies in chest CT scan, while ensuring the privacy of patient data. By exploring the delicate balance between privacy and accuracy in medical image classification, our study will contribute to the advancing realm of research.

关键词： Deep learning Data privacy Privacy Accuracy Federated learning Computed tomography Data models Medical diagnostic imaging Residual neural networks Image classification

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