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Real-time drone detection framework based on advanced texture feature extraction and pattern recognition model using GUI

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Neural Computing and Applications 2025年第5期37卷 3435-3454页

作者： Hussen, Noha Salem, Mofreh Eldesouky, Ali I. Sakr, Noha Elghamrawy, Sally Computers and Control Systems Engineering Department Mansoura University Mansoura Egypt Computer Engineering Department Misr Higher Institute for Engineering and Technology Mansoura Egypt

The increasing prevalence of drones has raised significant concerns regarding their potential for misuse in activities such as smuggling, terrorism, and unauthorized access to restricted airspace. Consequently, the development of robust and efficient drone detection systems has become paramount. Traditional detection methods such as radar and acoustic sensors have limitations in detecting small drones and can be costly to implement. This can lead to lower detection rates and increased security breaches and safety concerns. However, prevailing methodologies often falter in achieving real-time detection with requisite precision and efficacy, particularly within complex operational environments. Motivated by the imperative to surmount these challenges, our research endeavors to introduce a pioneering real-time drone detection framework. Our study encapsulates several seminal advancements. Firstly, we proffer a groundbreaking framework that synergizes advanced integration technique based on texture feature extraction and pattern recognition techniques for real-time drone detection to increase accuracy to detect drones in different conditions such as bad weather and low resolution. Secondly, we introduce an intuitive graphical user interface, enhancing the usability and accessibility of the system in real-time scenario. Lastly, through exhaustive evaluation and comparative analysis, we substantiate the superior performance of our framework in terms of accuracy, precision, and real-time detection capabilities compared to conventional DDS methodologies. Its stability and effectiveness render it a compelling solution for security-focused entities, notably those within air force and military systems. Our experimental results reveal a commendable accuracy rate of 97%, affirming the reliability and precision 98% and recall parameter 98% of our framework in accurately identifying and detecting drones, thus surpassing recent models in the field. © The Author(s), under exclusive

关键词： Graphical user interfaces

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A Deep Learning Technique for Multi-Classification of Acute Lymphocytic Leukemia

A Deep Learning Technique for Multi-Classification of Acute ...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： El-Seddek, Mervat Misr Higher Institute for Engineering and Technology Department of Communications and Electronics Engineering Mansoura Egypt

ISBN: (纸本)9798350351408

Early detection, diagnosis, prognosis, and treatment of blood cancers are challenging healthcare problems. Leukemia is a very fatal blood cancer. Leukemia can be categorized into acute leukemia and chronic leukemia. In childhood, acute lymphocytic leukemia (ALL) is responsible for about 25% of all childhood cancers. Because the microscopic images of acute leukemic b-lymphoblasts are very similar to those of benign cells under the microscope, it is very hard to stage cancer and differentiate them from benign cells. The present work proposes a computer-aided detection system to classify ALL into four classes, based on convolutional neural network (CNN) models, namely VGG16 and DenseNet201. These classes include normal cells, early lymphoid precursor (ELP), pro-B cells, and pre-B cells. The classification accuracy of the suggested approach had reached 99.66% and 99.9% for ALL multi-classification using VGG-16 and DensNet-201 respectively. The suggested system potentially improves the accuracy and speed of diagnosis to help pathologists and specialists analyze images and make the most appropriate decision to identify clinical cases of patients and start an early treatment approach. © 2024 IEEE.

关键词： Lung cancer

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Optimized Cooperative Caching Scheme for Mobile Ad-Hoc Networks Over IoT Systems

Optimized Cooperative Caching Scheme for Mobile Ad-Hoc Netwo...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ameen, Elmahdy M. MISR Higher Institute for Engineering and Technology Department of Communication and Compuer Engineering Mansoura Egypt

ISBN: (纸本)9798350351408

Mobile Ad-hoc Networks over the Internet of Things (MANET-IoT) systems combine Wireless Sensor Networks (WSNs) and Mobile Ad-hoc Networks (MANET)s with the Internet of Things (IoT) to enhance user mobility and reduce network deployment costs. This paper introduces an Optimized Cooperative Caching Scheme (OCCS) for caching in MANET-IoT systems. OCCS partitions the MANET-IoT into regions, where each region acting as a fog region managed by a Master Server (MS). The MS monitors and manages data objects, applying cache replacement policies to sink node (SN) in the region. Unlike traditional cache schemes, OCCS proactively manages cache without waiting for requests from SN nodes. By continuously monitoring data object status and exchanging this information among SNs. Experimental results show that OCCS efficiently utilizes memory capabilities and improves MANET-IoT performance in terms of network throughput, latency, and cache hit ratio. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Experimental study of the synergistic effect of crumb rubber and steel fiber on the flexural and impact performance of concrete beams

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Journal of Building Pathology and Rehabilitation 2025年第2期10卷 1-18页

作者： Saad, Ahmad G. Sakr, Mohammed A. Khalifa, Tarek M. Darwish, Esam A. Department of Structural Engineering Tanta University Tanta Egypt Department of Civil Engineering Misr Higher Institute for Engineering and Technology Mansoura Egypt

Concrete structural elements function effectively by exhibiting resilience under static and dynamic loads, effectively absorbing energy to prevent catastrophic failure. Recycling scrap tire rubber as aggregate offers an eco-friendly approach to improving the dynamic mechanical properties of concrete while addressing environmental concerns. This study investigates experimentally the behavior of rubberized concrete (RuC) under low-velocity repeated impact loading, focusing on the effect of replacing 10% of sand with crumb rubber (CR) to optimize the balance between enhanced impact resistance and slight reductions in static strengths. Additionally, the study evaluates the performance of RuC enhanced with 1% steel fiber (to mitigate strength reductions from rubber addition) and 1% superplasticizer. The study investigated the impact and flexural behavior of three concrete mixes: ordinary concrete, RuC with 10% crumb CR, and enhanced RuC (with 10% CR, steel fibers, and superplasticizer). Six beams (100 mm × 100 mm × 500 mm) were tested;three for repeated impact loading (7 kg drop mass from 450 mm) and three for three-point flexural testing. Compression and direct tensile tests were also conducted on each concrete mix to characterize their mechanical properties. The experimental results disclosed that incorporating 10% CR reduced the concrete compressive strength by 32.4%, but the addition of steel fibers and superplasticizer mitigated this reduction, restoring strength to near-control levels. The enhanced RuC exhibited superior flexural performance and damage resistance under impact loading, requiring significantly more blows to induce cracking and failure compared to both control and RuC. The results demonstrate that the blend of CR and steel fibers enhances toughness, ductility, and energy dissipation, making RuC a viable material for applications requiring superior impact resistance. This study additionally proposes a validated finite element model that accurately pred

关键词： Crumb rubber Numerical modeling Repeated impact loads Rubberized concrete Beams Steel fiber

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Structural Performance of Concrete Reinforced with Crumb Rubber: A Review of Current Research

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Iranian Journal of Science and technology - Transactions of Civil engineering 2024年 1-44页

作者： Saad, Ahmad G. Sakr, Mohammed A. Khalifa, Tarek M. Darwish, Esam A. Department of Civil Engineering Misr Higher Institute for Engineering and Technology Mansoura Egypt Department of Structural Engineering Tanta University Tanta Egypt

This review article investigates the structural performance of concrete reinforced with crumb rubber, specifically focusing on rubberized engineered cementitious composites (RECC), which present a sustainable alternative in construction materials. By incorporating recycled tire rubber as a partial substitute for conventional aggregates, RECC addresses the environmental challenges associated with tire waste while enhancing certain mechanical properties of concrete. This comprehensive review synthesizes findings from over 130 studies, revealing that RECC can reduce concrete density by up to 20% while improving ductility and impact resistance, although it may lead to a decrease in compressive strength by as much as 30%. Key findings highlight the performance of RECC across various structural elements, including beams, slabs, columns, beam-column joints, sandwich panels, and substructural elements such as piles. Notably, the RECC core in sandwich panels demonstrates higher load capacity and ductility compared to traditional foam cores. Additionally, confinement techniques significantly enhance the compressive behavior of rubberized normal concrete columns, improving both ductility and energy absorption. In beam-column joints, RECC exhibits enhanced ductility and energy dissipation, albeit with reduced strength, which can be mitigated through optimized mix designs incorporating materials like metakaolin or silica fume. Furthermore, crumb rubber shows promise in improving impact resistance and mitigating seismic forces, particularly enhancing the performance of piles in liquefiable ground conditions. Despite the promising attributes of RECC, gaps in the current understanding of its long-term durability, interfacial bond strength, and fire resistance necessitate further investigation. Future research should focus on advanced surface treatments for crumb rubber, optimization of mix designs with supplementary binders, and comprehensive studies on the long-term performance of

关键词： Beam-column joints Beams Composite columns Eco-friendly Piles Rubberized engineered cementitious composites Sandwich slabs

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An AI-Based Prediction Model for Climate Change Effects on Crop production using IoT

An AI-Based Prediction Model for Climate Change Effects on C...

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2023 International Telecommunications Conference, ITC-Egypt 2023

作者： Elghamrawy, Sally MISR Higher Institute for Engineering and Technology Computer Engineering Department Mansoura Egypt Egypt

ISBN: (纸本)9798350326062

Climate change influences agriculture's sustainability over different aspects. This paper proposes a prediction model grounded on Artificial intelligence techniques to predict the climate change effects on the Crop production. The proposed model is known as the Prediction Model for Climate Change Effects on Crop production (PMCCE). PMCCE utilizes the Artificial intelligence techniques along with the IoT technology to predict the crop production taking in consideration the climate change factors as: Annual Precipitation, Climate impacts per Celsius °C, Co2 concentrations, and temperature. The proposed model provides decision-makers with a comprehensive overview of the suitability of number of the most needed crops (namely, maize, wheat, and rice) for both short-term and long-term cultivation. An optimized Convolutional Neural Network(CNN) is proposed in PMCCE to predict the most appropriate crop based on climate change data and to predict the most significant factors affecting crop production. The experimental results indicate that the proposed model efficiently predicts the most suitable crop that enhances yield production and is better suited to cultivation under specific climate change scenarios. Furthermore, PMCCE outperforms other prediction models that used different methods, as the accuracy of the PMCCE model for the three targeted crops, achieved 98.2%, 98.69%, and 98.1% for Wheat, Maize, and Rice, respectively. Additionally, the PMCCE model identified the most significant factors in climate change data that affect crop production: CO2 and the average temperature were the most significant factors that affects the crops production, then climate impacts, climate scenario and adaptation type. © 2023 IEEE.

关键词： Climate change

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Nanotechnology and its impact on achieving sustainable architecture in Egypt

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Journal of Umm Al-Qura University for engineering and Architecture 2024年第2期15卷 138-161页

作者： Tohlob, Ahmed Al Hussein Morsi, Haitham Emad El-Din Architectural Engineering Department Misr Institute for Engineering and Technology Mansoura Egypt Jeddah International College Jeddah Saudi Arabia Architectural Department Gezira Higher Institute of Engineering and Technology Cairo Egypt

Considering the significant growth of the global population, several severe issues have emerged, including pollution, economic challenges, and environmental problems. To tackle these ecological issues, many construction experts are actively seeking new flexible techniques. Among these, Nano-technology stands out as a crucial field aiming to enhance the efficiency of the built environment and play a vital role in addressing future challenges. This study aims to examine the effectiveness of integrating Nano-technology techniques in construction to harness the acquired properties of materials and their impact on the interior thermal environment. The paper is divided into three main parts, each providing a detailed and scientific depiction of the study. The first part begins by shedding light on the concept of sustainability in architecture and the evaluation of sustainable buildings. Additionally, it explores the definition of Nano-technology and its substantial impact on the field of architecture by discussing various architectural terms associated with Nano-technology. The emergence of Nano-architecture is seen as a significant advancement in scientific research, representing a fusion between architecture and Nano-technology. Moving on to the second part of the thesis, it focuses on how Nano-technology techniques influence the work of architects. Nowadays, architects consider the properties of construction materials, construction methods, and energy consumption. These factors are essential in achieving an ideal design that is defined as sustainable. The second part also examines the effect of Nano-technology on the thermal environment, analyzing both national and international projects that have implemented this technique. © The Author(s) 2024.

关键词： Insulation materials Nanotechnology Simulation programs Thermal comfort Thermal environment

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Numerical analysis of laterally loaded barrettes performance in cohesionless soils

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Journal of engineering and Applied Science 2023年第1期70卷 51页

作者： Akl, Sherif A. Y. Ismail, Abdelrahman N. Abdelmoghni, Mahmoud Hegazy, Yasser A. Soil Mechanics and Foundations Research Laboratory Cairo University Cairo University Rd Giza Egypt Civil Engineering Department MISR Higher Institute for Engineering and Technology Mansoura Egypt

This paper investigates the performance of a single and a group of laterally loaded barrettes using a series of numerical models in a three-dimensional finite element software. The simulation of laterally loaded barrettes in both directions is verified by comparing numerical predictions to field measurements previously published in the literature. This paper defines the soil wedge around the barrette sheared at a strain equivalent to the minimum shear strain value at the barrette side or higher as the effective strain wedge. The results from a suite of numerical experiments show that the size of the effective shear strain wedge remains constant at different lateral loads, soil density, barrette shape (aspect ratio), and barrette stiffness. This paper also delineates the effect of spacing in a group of barrettes on the mobilized strain wedges and the associated lateral deformations. The paper then suggests equivalent dimensions for laterally loaded barrettes to be used as input parameters in analyses using p-y curves based on the shape of the effective strain wedge. The predictions from p-y analyses are improved in all the studied cases when equivalent dimensions are used compared to the finite element computations. The paper studies the appropriate design multipliers recommended in literature to be used in the p-y curves method for laterally loaded barrette groups in single and multiple rows. Comparisons with finite element results show the validity of employing the equivalent shape dimensions in determining suitable p-multipliers. © 2023, The Author(s).

关键词： Shear strain

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Design and Implementation of Advanced Driver Assistance Systems

Design and Implementation of Advanced Driver Assistance Syst...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Ali, Waleed Abd-Elshafi Saleh, Hatem Abdelhamid Eid, Haneen Salah Fathi, Ahmed Mohamed Mohamed, Abd-El Rahman Amr Higher Institute of Engineering EL- Shorouk Academy Department of Communications and Computer Engineering Cairo Egypt Misr University for Science and Technology Department of Electronics and Communications Engineering Giza Egypt

ISBN: (纸本)9798350351408

Advanced Driver Assistance Systems (ADAS) are a pivotal advancement in automotive technology, enhancing vehicle safety and driving experience. In our project, we have successfully designed and built a robotic prototype car. We have implemented a comprehensive control system for the car's movement, and the following features of ADAS systems: Bump Detection, Precise Lane Tracking, Adaptive Cruise Control, Traffic Sign Recognition, and Blind Spot Detection. Bump detection systems are equipped to detect and alert drivers of speed bumps and other road irregularities, in which we use NVIDIA Jetson Nano, Astra Pro Plus Depth camera as RGB and Depth sensor and use Custom AI Model based on YOLOv5 to detect different Bumps on the road and we use Python, TensorRT, and OpenCV. Lane Departure Warning (LDW) alerts drivers when their vehicle unintentionally drifts out of their lane, we use NVIDIA Jetson Nano as our main microcontroller unit (MCU) responsible for our Machine learning and Astra Pro Plus Depth camera as our RGB sensor, we use Python, Python's libraries such as OpenCV, and NumPy to implement LDW. Adaptive Cruise Control (ACC) adjusts the vehicle's speed automatically to maintain a safe distance from the vehicle ahead, we use NVIDIA Jetson Nano, Astra Pro Plus Depth camera as RGB and Depth sensor and use Custom AI Model based on YOLOv5 to detect different vehicles on the road, we use Python, TensorRT, and OpenCV. Traffic Sign Recognition can interpret and display traffic signs to keep drivers informed about speed limits, no-entry signs, and more, we use NVIDIA Jetson Nano, and Astra Pro Plus Depth camera as our RGB sensor and use Custom AI Model based on YOLOv5 to detect different Traffic Signs on the road, we use Python, TensorRT, and OpenCV. Blind Spot Detection (BSD) technology helps drivers identify vehicles in their blind spots and alerts them to avoid dangerous maneuvers, we use ESP32 as an MCU and Ultrasonic sensor, and use C++ to program the BSD feature. © 2024

关键词： Advanced driver assistance systems

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Plant Disease Detection using Artificial Intelligence techniques for Agricultural productivity enhancement in Egypt

Plant Disease Detection using Artificial Intelligence techni...

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2023 International Telecommunications Conference, ITC-Egypt 2023

作者： Ibrahim, Aya Osman Elghamrawy, Sally The British University in Egypt Computer Engineering Department Cairo Egypt MISR Higher Institute for Engineering and Technology Computer Engineering Department Mansoura Egypt

ISBN: (纸本)9798350326062

In the past few years, the use of artificial intelligence and especially Convolution Neural networks (CNN) for different computer vision tasks has been taking over. One of these tasks is detecting diseases in plants since plant diseases is now a topic of interest as it highly affects the crop yield production. Egypt in fact is one of the main countries that should consider plant disease detection for a productive crop yield since Egypt heavily relies on agriculture as it's income. Moreover, other countries also rely on the crops exported from Egypt such as oranges, as Egypt is considered one of the largest orange exporters in the world. Therefore, deep learning was stated as the current effective approach in smart agriculture to help tackle the crop yield reduction crisis that are caused due to the neglection of plant diseases and the standard visual inspecting techniques used for detecting plant diseases. In this paper different deep learning Convolution Neural Network approaches are proposed using the new plant diseases dataset which consists of 38 classes of 14 types of healthy and diseased crop leaf images to help detect diseases in plants effectively and efficiently. The first proposed approach is a Convolution Neural Network model consisting of four convolution and pooling layers in addition to two fully connected layers and an output layer with 38 neurons for each of the 38 classes which resulted in an accuracy of 98.36%, loss of 0.0972, 98.35% F1 score, 98.36% recall, 98.39% precision, model size of 12.25mb, training time of 22.82 seconds, and total parameters of 668, 134. While the second approach is a Hybrid ResNet50-InceptionV3 model with an accuracy of 96.20%, a precision of 96.16%, and F1-score of 96.45%. © 2023 IEEE.

关键词： Crops

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