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Optimizing potato leaf disease recognition: Insights DENSE-NET-121 and Gaussian elimination filter fusion

Heliyon 2025年第3期11卷 e42318页

作者： Raza, Asif Pitafi, Abdul Hammed Shaikh, M. Kahsif Ahmed, Khaliq Universiti Kuala Lumpur Malaysian Institute of Information Technology (UniKL MIIT) 1016 Jalan Sultan Ismail Kuala Lumpur 50250 Malaysia Department of Computer Science and Information Technology Sir Syed University of Engineering & Technology Karachi Pakistan Department of Software Engineering Sir Syed University of Engineering & Technology Karachi Pakistan Department of Computer Science Iqra University Karachi Pakistan

Ensuring a sustainable global food security status which necessitated by achieving an equilibrium state between the anticipated and significant rise in the global population and the projected agricultural output which is essential for their food adequacy. The absence of such a harmonious balance may be a contributing factor to the emergence of food crises worldwide. Hence, it is imperative to proactively address and mitigate both direct and indirect factors that could potentially lead to this agricultural yield imbalance. Facilitating optimal plant growth and implementing effective measures against diseases play a fundamental role in meeting the global demand for food in terms of both quality and quantity. This article offered a hybrid model based on Deep learning called DENSE-NET-121 with 2D Gaussian elimination filters that can be effective deep learning tools to increase potato yield by early detection of the leaf. Three types of potato leaf classes called Early Blight, Healthy, and Late Blight are incorporated by Dataset which has been taken from the kaggle repository. Considering this proposed model, state-of-the-art DENSE-NET-121 has produced an unprecedented training and validation accuracy 0.9908, 0.9837 respectively furthermore model also produced extremely low training and validation loss 0.0683, 0.0796 and an error rate below then 0.1 as well. Furthermore model produced average Precision, and recall, 0.98, 0.96, and 0.97 respectively. © 2025 The Authors

关键词： 2D Gaussian filter Deep learning DENSE-NET 121 Machine learning Potato leaf

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Performance Analysis & Latency Calculation Of Cooperative Communication Protocols

Performance Analysis & Latency Calculation Of Cooperative Co...

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

作者： Noreen Akram Ayesha Rafique Madiha Abbasi Asif Hasan syed Rehan Ali Zaidi Telecommunication Engineering Department Sir Syed University of Engineering and Technology Karachi Pakistan Air Engineering Department PNS Mehran Karachi Pakistan

Wireless communications have been remarkably & rapidly evolving during last decade. This research work is focused on the Capacity, Throughput and Outage Probability of Cooperative communication protocols and the total latency caused by Cooperative diversity systems. The objectives of this work are as follows: To calculate the throughput; to calculate the probability; Analysis and evaluations of total latency caused by Cooperative diversity systems. For the research methodology, firstly it is discussed that why cooperation is needed and how cooperation is possible in a wireless network. Then the throughput, outage probability, various types of delays, latency requirement has been discussed. Finally, a system model with single and multiple relays has been presented. For analyzing the performance of Cooperative Communication system, MATLAB tool is used as simulator. The simulation parameters are: Channel gains with additive white Gaussian noise (AWGN); BPSK modulation. Currently most of the work is based on analyzing performance improvement by cooperation but not on its possible side effects. Latency and security needs to be addressed carefully. That is why designing a laboratory based wireless cooperative communication prototype could be the best way to determine how much performance enhancement it can bring in real and practical wireless environment.

关键词： Protocols Cooperative communication Wireless networks Prototypes Probability Throughput Power system reliability

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Enhancing Smart Mobility Through Machine Learning and Neural Networks

Enhancing Smart Mobility Through Machine Learning and Neural...

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

作者： Muhammad Nadeem Waleej Haider Haris Ahmed Sallar Khan Department of Computer Science & Information Technology Sir Syed University of Engineering & Technology Karachi Pakistan School of Informatics & Cybersecurity Technological University Dublin Dublin Ireland

ISBN: (数字)9798331534271

ISBN: (纸本)9798331534288

This study investigates the prediction of taxi trip durations in New York City using machine learning (ML) models and neural networks (NN). Three models Linear Regression, Random Forest Regressor, and a Neural Network were developed and evaluated on a dataset containing various trip details. The analysis highlighted the importance of geographic and temporal features in predicting trip durations. The Neural Network model outperformed others, achieving a mean squared error (MSE) of 12,577,211 and a root mean squared error (RMSE) of 3,546, significantly better than the comparative models. These results underscore its capacity to capture complex urban mobility patterns effectively. The superior performance of the Neural Network supports its potential to optimize traffic flow and reduce congestion in dense urban environments, offering substantial improvements to urban transportation systems.

关键词： Wireless communication Soft sensors Neural networks Linear regression Urban planning Predictive models Data models Socioeconomics Random forests Public transportation

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Customized Efficientnet-B6 for Brain Tumor Detection: Enhanced Accuracy with Bayesian Optimization and Deep Learning

SSRN

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SSRN 2025年

作者： Natha, Sarfaraz siraj, Mohammad Altamimi, Majid Shah, Adamali Zahoor, Adnan Department of Software Engineering Sir Syed University of Engineering and Technology Karachi75300 Pakistan Department of Electrical Engineering College of Engineering King Saud University Riyadh11543 Saudi Arabia

A brain tumor is a mass of tissue that grows out of control of the normal forces that regulate growth inside the brain. A brain tumor appears when one type of cell changes from its normal characteristics and multiplies abnormally. The unusual growth of cells within the brain or inside the skull. The precise early detection of brain tumors via magnetic resonance imaging (MRI) is a difficult problem for researchers in medical imaging applications. The mortality rate from fatalities caused by brain cancer is decreased by early tumour diagnosis. Numerous deep learning-based medical image processing approaches have been presented for brain MRI image diagnosis and health monitoring. An area of deep learning called CNNs (Convolutional Neural Networks) is frequently used to analyze visual data. In this study, we proposed a customized pre-trained EfficientNetB6 (CPEB6) that is based on a CNN model using EfficientNet-B6 as the base model, strengthened with fine-tuned and customized layers to enhance the classification process of MR images. The removal of noise or unwanted artifacts from signals and images by using the fastNlMeansDenoising() method. Furthermore, data augmentation methods are used to prevent the model from overfitting. The Bayesian Optimization technique is also used to optimize decision-making on setting parameters by applying an objective function to understand past settings, ultimately finding the best hyperparameter settings efficiently compared to grid and random searches. The proposed model provides remarkable results with 98.98% accuracy, 98.97% precision, 98.96% recall, 98.98% F1-score, and 98.98% specificity, outperforming previous state-of-the-art methods. © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A Survey on Topology-Aware Routing Protocols for Underwater Optical Wireless Sensors

A Survey on Topology-Aware Routing Protocols for Underwater ...

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

作者： Madiha Abbasi M.-À. Luque-Nieto Telecommunication Engineering Department Sir Syed University of Engineering and Technology Karachi Pakistan Institute of Oceanic Engineering Research University of Malaga Málaga Spain

ISBN: (数字)9798331534271

ISBN: (纸本)9798331534288

In recent years, there has been a growing interest in UWSNs as a viable networking strategy for use in a variety of aquatic settings. Improvements in data transmission efficiency and the practical use of routing systems are both made possible via the minimization of energy usage. Due to its fluid nature, limited bandwidth, quick energy depletion, and severe delay, Underwater Wireless Sensor Networks (UWSNs) deliver major hurdles for the development of routing systems. In this paper, we'll take a look at the different strategies to routing in UWSNs. There are now two main groups of routing protocols in use across the world, and each has its own route decision maker. The effectiveness of various routing options is then analyzed by means of a comprehensive review. Additionally, a thorough analysis is performed to investigate prospective areas for further study on routing methods in UWSNs.

关键词： Wireless communication Surveys Wireless sensor networks Reviews Optical fiber networks Routing Minimization Routing protocols Optical sensors Resilience

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Fingerprint generation and authentication though Adaptive convolution generative adversarial network (ADCGAN)

Fingerprint generation and authentication though Adaptive co...

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International Multi-Topic ICT Conference (IMTIC)

作者： syed Muhammad Nabeel Mustafa syeda Sundus Zehra Alina Baber Maria Andleeb Siddiqui Dept. of Computer Science & Information Technology NED University of Engineering and Technology Karachi Pakistan Dept of Software Engineering Sir Syed University of Engineering and Technology karachi Pakistan

Fingerprints are crucial in identification of humans. The uniqueness of finger prints makes it an interesting subject. Fingerprints are termed as a technique used to define, assess, and quantify a person's physical and behavioral property. Deep learning has made its application in all the major fields such as natural language processing, computer vision and speech processing. Deep learning has also found its application in the important subject of fingerprint synthesis and biometric. The ever-growing complexity of fingerprint authentication issues, from cellphone authentication to airport security systems, seems to be best handled by these models. In recent years, deep learning-based models have been used more and more to raise the accuracy of various fingerprint recognition systems. The persuasive capacity of Generative Adversarial Networks (GANs) to generate believable instances can be credibly taken from an existing distribution of samples. GAN exhibits exceptional performance on data generation-based tasks and also encourages study in privacy and security. In this work, using Adaptive Deep Convolution Generative Adversarial Networks (ADCGAN), we develop a model that generates and authenticate the fingerprints. A Socofing dataset was trained on ADGAN model. The model gave 92% accuracy. The conduct of fingerprint research has been made possible due to ADGAN, without restrictions related to the confidential nature of biometric data.

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A novel approach for handling missing data to enhance network intrusion detection system

Cyber Security and Applications

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Cyber Security and Applications 2025年 3卷

作者： Tahir, Mahjabeen Abdullah, Azizol Udzir, Nur Izura Kasmiran, Khairul Azhar Department of Computer Science & Information Technology University Putra (UPM) Selangor Serdang 43400 Malaysia Department of Computer Engineering Sir Syed University of Engineering & Technology Sindh Karachi 75300 Pakistan

Managing missing data is a critical challenge in Intrusion Detection System (IDS) datasets, significantly affecting the performance of deep learning models. To address this issue, we introduce DeepLearning_Based_MissingData_Imputation (DMDI), a novel method designed to enhance the quality of input data by efficiently handling missing values. Our approach employs the Random Missing Value (RMV) algorithm to simulate missing data, enabling thorough testing and comparison of various imputation techniques. The DMDI method integrates a stacked denoising autoencoder with Gradient Boosting to improve imputation accuracy. We evaluated the effectiveness of our approach through three experimental phases: generating missing data, imputing missing values, and assessing imputation models. Using the NSL-KDD and UNSW-NB15 datasets, our results demonstrate significant improvements in the performance of five different classifiers (SVM, KNN, Logistic Regression, Decision Tree, and Random Forest) after imputation. On average, our method achieved accuracy improvements ranging from 0.95 to 0.97 across these classifiers compared to baseline imputation methods. Detailed analysis using Python 3 validates our findings, demonstrating enhanced model performance and robustness. This study underscores the necessity of precise missing data imputation for enhancing deep learning tasks, particularly in anomaly detection systems. It provides a reliable solution for managing missing data in IDS datasets. © 2024

关键词： Autoencoder Gradient Boosting Intrusion detection Machine learning Missing data Imputation Neural Networks

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Framework for Live Migration of FPGA based ECB-mode AES-128 accelerator 2

Framework for Live Migration of FPGA based ECB-mode AES-128 ...

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2nd International Conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Asfia, Yame Khawaja, Sajid Gul Asad, Muhammad Mirza, Alina Ceme National University of Sciences and Technology Department of Computer & Software Engineering Islamabad Pakistan Sir Syed Case Institute of Technology Department of Computer Engineering Islamabad Pakistan National University of Sciences and Technology Department of Electrical Engineering Mcs Islamabad Pakistan

ISBN: (纸本)9781665498197

Virtual Machines in cloud datacenters are live migrated for assuring service availability in the event of errors and maintenance. FPGA based hardware accelerators must also be live migrated for the same reasons. AES is widely accepted encryption standard and is therefore commonly used for security provision. In this paper, we propose a novel live migration technique for migration of FPGA accelerated ECB mode AES-128 encryption algorithm using the concept of pipelining which gracefully migrates the application to the destination. The proposed methodology is explained with the help of outer-round only pipelined design of AES-128. Experiments show that the proposed method can reduce downtime nearly to zero, ensuring the service's availability at maximum. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Enhancing Agricultural Productivity: A Sensor Fusion, Spectroscopic Near Infrared, and AI-Based Framework for Monitoring Fruit Ripeness 21

Enhancing Agricultural Productivity: A Sensor Fusion, Spectr...

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21st International Bhurban Conference on Applied Sciences and technology, IBCAST 2024

作者： Ambreen, Hira Imran, Muhammad Diyin, Tang Zhang, Chongsheng Sir Syed CASE Institute of Technology Islamabad Pakistan School of Automation Science and Electrical Engineering Beihang University Aeronautics and Astronautics China Henan Univerity Department of EE Kaifeng475001 China School of Automation Science and Electrical Engineering Beihang University Aeronautics and Astronautics Department of Detection and Automation Engineering China School of Computer & Information Engineering Henan University Kaifeng475001 China

ISBN: (纸本)9798331516680

Fruit ripeness evaluation is essential for harvest optimization and post-harvest loss prevention. Traditional visual inspection is inefficient. This study employs sensor fusion and AI algorithms to evaluate fruit maturity in real-time using gas sensors (i.e., MQ-3, MQ-6, MQ-8, MQ-135) and Near-Infrared spectral sensors (i.e., GY-AS7263). Primarily, data pro-cessing-sensor fusion, data preparation, and AI model application-is crucial to the proposed framework. Subsequently, sensor fusion approaches improve data accuracy by combining signals from various sensors. Secondly, data pretreatment techniques including noise reduction, normalization, and feature extraction prepare data for analysis. Thirdly, we use powerful machine learning algorithms such as Random Forest to judge maturity more accurately and reliably than conventional techniques. Lastly, integrating NIR spectroscopy with gas sensor data resolves cost and model transferability issues, enhancing accuracy and acces-sibility for agricultural use. Static image processing techniques provide challenges for real-time applications, including expensive computer costs and lower reliability. The proposed framework based on multi-sensor fusion system enhances agricultural applications' versatility, scalability, and reactivity, surpassing existing methodologies. © 2024 IEEE.

关键词： Fruits

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Analyzing the Classification Performance of DenseNet121 on Pre-processsed MIAS Dataset

Analyzing the Classification Performance of DenseNet121 on P...

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

作者： Tauseef Mubeen Zain-Ul-Abidin Muhammad Huzaifa Shahbaz Pablo Otero Roth Miguel Angel Luque Nieto Software Engineering Department Sir Syed University of Engineering and Technology Karachi Pakistan Department of Telecommunication Engineering University of Malaga Malaga Spain

Breast cancer is the most diagnosed type of cancer as per the data that is collected by the World Health Organization (WHO) within the past few years. Over 600,000 deaths were recorded in 2021 due to breast cancer. Breast cancer screening is done using two-dimensional (2D) and three-dimensional (3D) mammography, but MRIs and Ultrasounds are also used in certain conditions. The diagnosis from the screenings is not always accurate as a practitioner must physically look at the digital images to find any signs of cancer. Approximately, each diagnosis has a variable chance of a false-positive or a false-negative. Many CAD (computer-aided detection) systems have been developed for the assistance of a practitioner with the diagnosis. However, in the past years, Deep Neural Networks (DNN) have seen a spike and the models are being used to aid breast cancer screening. Data shows a possibility of reaching Area under the curve (AUC) values as high as 0.99 under ideal conditions when the training dataset is cleaned of noise and properly pre-processed and in some studies, the accuracy and sensitivity are even compared to that of a practitioner’s, with the DNN model outperforming in numbers across the board. After performing a literature review on similar work, we have trained a model of our own on a publicly available dataset (MIAS) reaching promising results of an AUC of 0.87 and an Accuracy of 0.88 with the initial model built on a DenseNet121 architecture.

关键词： Wireless communication Training Solid modeling Ultrasonic imaging Three-dimensional displays Sensitivity Neural networks

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