Computer Engineering Commons^™

Hybrid Methods For Detecting Face Morphing Attacks, Essa M. Namis, Khalid Shaker, Sufyan Al-Janabi

Iraqi Journal for Computer Science and Mathematics

The face morphing process blends two or more facial images to produce a singular morphed facial image that shows the vulnerabilities of Face Recognition Systems (FRS). The widespread use of facial recognition algorithms, especially in Automatic Border Control (ABC) systems, has elicited concerns about potential attacks, as modified passports pose a significant risk to national security. This research presents a hybrid approach for feature extraction from facial images. The suggested approach involves three stages: The initial phase involves preprocessing the image through resizing and face identification, using the Viola-Jones algorithm to detect and locate the human face in the image, …

Finding General Mathematical Formulas For Extraction The Minimal Path Sets Of Complex Parallel-Series Networks, Mariem Hassan Lafta, Zahir Abdul Haddi Hassan

Iraqi Journal for Computer Science and Mathematics

Most real-world technological systems are highly complex, making it challenging to examine their reliability. Many systems can be represented as Complex Parallel-Series Networks (CPSN). The large number of components and subnetworks, along with their intricate connection, complicates the identification, evaluation, and potential failure of the CPSN. A minimal path set is a minimal set of components whose proper functioning (success) guarantees the success (operability) of the system. The set is minimal in the sense that removing any component from it means it no longer guarantees system success. The primary research problem is to identify these minimal path sets, both for …

A Beginner’S Guide To Artificial Intelligence (Ai) And Generative Ai (Gen Ai) For Small Businesses, Stanley Mierzwa, Iassen Christov

Center for Cybersecurity

Generative Artificial Intelligence (GenAI) guide for small businesses.

Support and funding for this effort was received from the United States Small Business Administration (Contract 73351023C0016) for this activity and research. Formulated and ultimately created as a result of this grant was the New Jersey Cybersecurity Regional Cluster (NJCRC), which included the partners of NTouch-BCT Strategies, Covenant Business Concepts, and the Kean University Center for Cybersecurity. As part of the free cybersecurity risk assessments offered to small businesses in the community, organizations were introduced to GenAI through a demonstration, with the key information provided in this booklet.

Generalized Plant Disease Detection Using Residual Networks With Eca And Senet Integration, Asmaa Aly Hagar, Marwa Reda Bastwesy, Reda Elbasiony, Mohamed Talaat Faheem

Journal of Engineering Research

Early and accurate detection of plant leaf diseases is crucial for safeguarding agricultural productivity and ensuring food security. Traditional methods of plant disease detection, which often rely on manual inspections and specialized models, encounter challenges such as limited scalability, data annotation difficulties, and task-spe-cific constraints. This paper introduces two innovative and general-ized approaches for plant disease detection by combining Residual Networks with channel attention modules. The first approach inte-grates ResNet-101, the Efficient Channel Attention (ECA) mecha-nism, and the Squeeze-and-Excitation Network (SENet), while the second combines ResNetRS-101 with the ECA mechanism. These models leverage the strengths of ResNets, the dynamic channel …

A Generalized Plant Disease Detection Technique Based On Residual Network With Efficient Channel Attention, Asmaa Aly Hagar, Marwa Reda Bastwesy, Reda Elbasiony, Mohamed Talaat Saidahmed

Journal of Engineering Research

The detection of plant diseases a vital task for ensuring crop health and optimizing agricultural productivity. Machine learning and computer vision have significantly advanced the automation of plant disease detection. However, traditional methods face several challenges, particularly with data annotation and the limitations of task-specific models, which often fail to generalize across different plant diseases. These methods are hindered by their reliance on models tailored to individual crops and the ongoing need for manual inspections, leading to inefficiencies and restricted scalability. This study proposes a method designed to enable the efficient and accurate identification of a broad range of plant …

Role Of Eye-Tracking Technology And Software Algorithms In Enhancing Adhd Detection And Diagnosis: A Systematic Literature Review, Lauren E. Perkins

Honors College Theses

This systematic literature review explores the role of eye-tracking technology and software algorithms in enhancing the detection and diagnosis of ADHD. ADHD, a neurodevelopmental disorder affecting both children and adults, is traditionally diagnosed through behavioral assessments, which may lack objectivity. Recent studies suggest that eye-tracking, specifically focusing on saccades, fixations, and blink rates, offers the potential for more accurate and objective measures of ADHD. The review examines clinical trials, observational studies, and machine learning research to assess the correlation between ADHD and eye movement patterns. Results indicate that individuals with ADHD exhibit distinct eye movement patterns, which can be quantified …

Optimizing Parameters For Efficient Computation With Fully Homomorphic Encryption Schemes, Cavi̇dan Yakupoğlu Karaağaç, Kurt Rohloff

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, we aim to provide a parameter selection approach for the BFVrns scheme, one of the prominent fully homomorphic encryption (FHE) schemes. Selecting parameters for lattice-based FHE schemes poses a practical challenge for both experts and nonexperts. To solve this problem, we introduce a hybrid approach that combines theoretical approach with experimental analysis. First, we employ regression analysis to examine the impact of parameters on both performance and security. The varying behavior of FHE parameters in terms of performance, security, and ciphertext expansion factor (CEF) makes parameter selection more challenging. To address this issue, we employ a multi-objective …

Decomposition Lstm With Dual Multi-Head Self-Attention For Wind Turbine Drivetrain State Forecasting, Haikun Jia, Huini Sun, Shuang Bai

Turkish Journal of Electrical Engineering and Computer Sciences

Due to the clean and renewable nature of wind energy, accurate prediction of rotor loads and operating states for wind turbine units has become of paramount importance. Currently, traditional methods relying on expert analysis combined with instrument testing for qualitative reasoning are both time-consuming and labor-intensive, and their accuracy guarantees are limited. In response to wind farm data entailing the interweaving of data from multiple sources and the diverse interrelations across various features and time steps, this study introduces a method for predicting rotor loads and operating states. Initially, we employ an iterative multi-scale seasonal-trend decomposition block to capture latent …

Fuzzy-Virtual Inertia Control To Improve The Frequency Response Of Multi-Area Power Systems, Nourelhouda Djaraf, Yacine Daili, Abderrahim Zemmit, Abdelghani Harrag

Turkish Journal of Electrical Engineering and Computer Sciences

Virtual inertia control (VIC) is essential for power systems dominated by electronic devices to compensate for the lack of inertia and ensure frequency regulation. However, most existing VICs often focus solely on optimizing the virtual inertia parameter to adapt to the high penetration of renewable energy sources (RESs) without considering the damping factor. This oversight can lead to significant fluctuations and power mismatches, especially in interconnected systems where the coordination between MGs is sensitive and essential, and there is a risk of propagation of deviations between MGs, which makes the control more complex. To address these issues, this paper presents …

Enhancing Spatial-Temporal Video Prediction With Ts-Vq-Vae: A Novel Encoder-Processor-Decoder Approach, Mei Feng, Fan Li

Turkish Journal of Electrical Engineering and Computer Sciences

Video prediction is a significant and actively researched area within the data science community. Its primary objective is to generate future video frames based on historical frames, finding applications in diverse domains such as human motion prediction, climate change analysis, and traffic flow forecasting. Traditional methods combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to capture complex correlations in spatial-temporal signals. Recent methods improve video prediction accuracy by introducing external information such as optical flow, semantic maps, and human pose data. However, these methods have limitations, such as not fully exploring the intermediate states of learning representations, overlooking …

Conversational Open-Domain Question Answering For Resource-Constrained Languages, Emrah Budur, Tunga Güngör

Turkish Journal of Electrical Engineering and Computer Sciences

The growing interest in Conversational AI has led to the development of Conversational OpenQA systems as a crucial step for meeting users' information needs in real world scenarios. Conversational OpenQA systems enhance standard OpenQA performance by leveraging conversation history of the users. However, building effective Conversational OpenQA systems requires large-scale Conversational OpenQA datasets, often limited to the English language, hindering progress in low-resource languages. We present a robust Conversational OpenQA system enhanced by conversational context, designed for languages with limited resources and exemplified in our case study for Turkish. To address data limitations in a cost-effective way, we repurpose existing …

A Case Study Of Gray-Box Fuzzing With Byte- And Tree-Level Mutation Strategies In Xml-Based Applications For Exposing Security Vulnerabilities, Şerafetti̇n Şentürk, Vahi̇d Garousi, Nejat Yumuşak

Turkish Journal of Electrical Engineering and Computer Sciences

Fuzzing is an automated process for detecting crashes and vulnerabilities in software system and it is classified as grammar- or mutation-based in terms of input generation. While the grammar-based fuzzing generates inputs from a specification and takes highly-structured inputs, mutation-based fuzzing generates inputs by modifying input files and abstract syntax trees randomly. There are not many case studies comparing the crash detection capabilities in the scope of mutation-based fuzzing. To add to the body of empirical evidence in this area, this case study compares fuzzing with different mutation strategies to evaluate their effectiveness in three aspects: fault detection effectiveness, fault …

Multikernel Embedded Fusion Unet (Mkef-Unet): A Robust Deep Learning Approach For Accurate Segmentation Of Chagas Parasites, Preet Kumar, Carlos Brito-Loeza, Lavdie Rada

Turkish Journal of Electrical Engineering and Computer Sciences

This paper introduces a novel approach for segmenting Chagas parasites on stained blood smear samples from mice during the acute phase of infection with Trypanosoma cruzi utilizing a U-Net-based deep learning model named multikernel embedded fusion UNet (MKEF-UNet). Our proposed model incorporates DenseNet-121 for feature extraction, a classifier module for predicting parasite information, and a segmentation decoder with multiscale feature fusion to generate precise segmentation results. Notably, the integration of the embedded vector module, multikernel convolutions with dilations, and advanced data augmentation techniques significantly enhance the model’s robustness and generalization capabilities. In extensive experiments on the Chagas dataset, MKEF-UNet achieves …

Fine-Grained Traffic Flow Inference Model Based On Dynamic Back Projection Network, Ming Xu, Guangyao Qi, Geqi Qi

Journal of System Simulation

Abstract: To solve the problem of large errors in the inference results of existing fine-grained urban flow inference models in complex traffic areas, a fine-grained traffic flow inference model based on dynamic back-projection network is proposed. The multi-dimensional interaction between the input coarse-grained traffic flow and external factors is calculated, and the interaction results are dynamically and adaptively fused with the coarse-grained traffic flow, so that the features can interact and adjust each other to assist model reasoning. Combining deep convolution and self-attention mechanism to learn local information and global information, and improve the understanding of input data by subsequent …

Enhancement Of Hardware-In-Loop Simulation Ability For Homing Guidance Through Adaptive Field-Of-View Method, Shizheng Wan, Yu Cheng, Xu Zhang, Xuwei Fan

Journal of System Simulation

Abstract: Considering homing guidance test in the hardware-in-loop simulation, commands of flight simulator and antenna array are likely to exceed their ranges when the target vehicle maneuvers with a large cross range. To solve this problem, the adaptive field-of-view method is proposed to enhance simulation ability in laboratory. Inflight aircraft attitudes and missile-target line-of-sight angles are chosen as state parameters, and the optimal performance function can be established with maximum servo angle of both flight simulator and antenna array. Gradient descent algorithm is applied to acquire the optimal bias angles between the laboratory coordinate system and the launch inertial coordinate …

Research On Pedestrian Avoidance Strategy For Agv Based On Deep Reinforcement Learning, He Wang, Jianing Xu, Guangyu Yan

Journal of System Simulation

Abstract: To ensure the safety and comfort of pedestrians during Automated Guided Vehicle (AGV) obstacle avoidance in smart factory environments, a deep reinforcement learning-based end-to-end obstacle avoidance method is proposed. The YOLOv8 module is introduced to extract pedestrian pose information, and a visual-based state space is designed. A reinforcement learning mechanism is formulated based on personal space theory, penalizing AGV behaviors such as entering pedestrian comfort space and collisions. A virtual simulation system is constructed, utilizing PPO algorithm along with LSTM network layer for obstacle avoidance strategy training and simulation experiments. Simulation results indicate that this obstacle avoidance strategy, under …

Economic Optimal Scheduling Of Microgrid Considering Elastic Recovery, Jianghong Chen, Kanghao Shi, Jiahui Hu, Xiaohan Zhao

Journal of System Simulation

Abstract: To enhance the ability of microgrids (MGs) to withstand extreme disaster events, this paper proposes a multi-objective scheduling model considering resilience restoration and economic performance, based on the traditional concepts of power system resilience and reliability. Resilience is specifically quantified. The model integrates energy storage into the objective function and includes reliability indicators as constraints, building on traditional microgrid economic dispatch. The optimization problem is solved using an improved white shark optimizer (WSO) and multi-objective fuzzy programming, where different weights are assigned to each objective function, and the optimal weights are determined through case studies. A microgrid scheduling scheme …

Parallel Task Transmission And Processing Optimization Scheme For Uav-Assisted Internet Of Vehicles, Chao Yang, Ruiqun Zheng, Zhen Li, Hongwei Zhang, Yanqun Tang, Dongze Li

Journal of System Simulation

Abstract: To address the increasing of computation demands of internet of vehicles (IoV) users due to the sudden traffic congestion, unmanned aerial vehicles (UAVs) are introduced to the intelligent transportation systems (ITS) to construct an UAV-assisted IoV network. The UAV limited energy and computing resources lead to the current traditional UAV coverage strategy with one by one less efficiency. We propose a parallel task transmission and processing optimization strategy, considering the line-of-sight communication links and fast moving characteristics of UAV. After receiving the tasks from vehicles in the service point, UAV can fly to the next point and perform task …

Three-Way Decision Clustering Algorithm Fusion Of Mutant Fireflies Algorithm, Zhaobin Li, Jun Ye, Haoyan Zhou, Yixin Wang, Yuzhen Han

Journal of System Simulation

Abstract: To address problems such as the premature phenomenon in the three-way clustering algorithm caused by the random selection of initial cluster centers and the need for repeated experiments to determine the value of q in the q-nearest neighbor concept, a three-way clustering algorithm optimized by a variant of the firefly algorithm is proposed. The firefly algorithm is employed to solve the problem of sensitivity to initial cluster centers. The target function value is taken as the brightness intensity of firefly to search the clustering center point, and the optimal solution is taken as the clustering center of the algorithm …

Research On Improved A* Algorithm Path Planning Based On Global Key Point Extraction, Guijuan Lin, Zihan Li, Yu Wang

Journal of System Simulation

Abstract: To address the limitations of the traditional A* algorithm in large and complex scenes, including traversing a large number of nodes, long computation times, and susceptibility to U-shaped traps, this paper proposes an improved A* algorithm incorporating the jump point search (JPS) concept and image processing techniques to extract key points from the global map. The proposed method preprocesses the global map to identify corner points located one grid diagonally from obstacles, constructs a key point list, and replaces the nodes traditionally traversed by the A* algorithm with these global key points, significantly reducing computational overhead. The neighbor nodes …