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3D model visualization function for responsive web design
(2023) Kamal, Sora Nazhan; Ibrahim, Abdullahi Abdu
Except for critical basic implementations seen in historical records, the abundance of similar frameworks, platforms, and apps available may be deceptive. Striking a balance between 3D certified displays its a basically vital tool for today’s quantitative simulation science. Since the seeing is the main sense for humanities. and incorporated complicated features continues to be difficult. Geometrically precise point clouds and meshes are the result of cutting-edge methodology (computer vision, reverse engineering, and digital photogrammetry), this technology (LiDAR, laser scanners, and unmanned aerial vehicles), and random research approaches. These represent random research approaches a by-product of the procedure.. As the precision, size, and complexity of these things rise, the capabilities of present technology to manage them deteriorate. In this research, we present a way for creating a low-cost 3D visualization platform that can perform tasks interactively. Moreover, we seek to verify the methodological aspect of implementing steps and simplified algorithms in a virtual reality like, the CGA module was used, which is a procedural methodology; by using MATLAB software is based on the procedural modeling methodology, which is based on the creation of an algorithm with rules according to what you want to obtain as parameters in the final modeling. Thus, the development of the 3D modeling generation algorithm begins, containing the necessary rules for the automatic calculation of the parameters.
A hybrid model for the prediction of electrical energy consumption using hybrid LSTM and ML regressors
(Institute of Electrical and Electronics Engineers Inc., 2024) Alsabbagh, Yahya Hafedh Abdulameer; Ibrahim, Abdullahi Abdu
Accurate forecasting of energy consumption over long periods is extremely important for companies that distribute and supply electricity, whether from the government or private sector. It is necessary in terms of improving the quality of energy production in the future, especially in countries like Iraq that have been suffering from an energy crisis for a long time. This study used electricity consumption data from the Ministry of Electricity in Iraq for the city of Baghdad, specifically the Rusafa area, for the years from 2021 to 2023. In this study, several models were worked on and compared with the proposed hybrid model (CNN-Stacked Bi-LSTM) with RF and KNN to achieve better performance in classification or prediction tasks. To predict future electricity consumption and improve the quality of energy production, the models were trained on electrical energy consumption data. We trained the models on (30) epochs, taking the MAPE and RMSE resulting from our assessment of the quality of energy consumption. The experiments found that the best results is the hybrid model using RF regressor, which produced a result of MAPE: 0.195046, RMSE: 0.101919 and MAE: 0.078101.
A hybrid model using 1D-CNN with Bi-LSTM, GRU, and various ML regressors for forecasting the conception of electrical energy
(World Scientific Publishing, 2025) Abdulameer, Yahya Hafedh; Ibrahim, Abdullahi Abdu
To solve power consumption challenges by using the power of Artificial Intelligence (AI) techniques, this research presents an innovative hybrid time series forecasting approach. The suggested model combines GRU-BiLSTM with several regressors and is benchmarked against three other models to guarantee optimum reliability. It uses a specialized dataset from the Ministry of Electricity in Baghdad, Iraq. For every model architecture, three optimizers are tested: Adam, RMSprop and Nadam. Performance assessments show that the hybrid model is highly reliable, offering a practical option for model-based sequence applications that need fast computation and comprehensive context knowledge. Notably, the Adam optimizer works better than the others by promoting faster convergence and obstructing the establishment of local minima. Adam modifies the learning rate according to estimates of each parameter's first and second moments of the gradients separately. Furthermore, because of its tolerance for outliers and emphasis on fitting within a certain margin, the SVR regressor performs better than stepwise and polynomial regressors, obtaining a lower MSE of 0.008481 using the Adam optimizer. The SVR's regularization also reduces overfitting, especially when paired with Adam's flexible learning rates. The research concludes that the properties of the targeted dataset, processing demands and job complexity should all be considered when selecting a model and optimizer.
A lightweight image encryption and blowfish decryption for the secure internet of things
(Institute of Electrical and Electronics Engineers Inc., 2020) Saddam, Mohanad Jalal; Ibrahim, Abdullahi Abdu; Mohammed, Alaa Hamid
As an innovative technology of the future, the Internet of Things (IoT) is expected to connect billions of users. Increased connectivity is expected to generate mountains of data and data protection can be a threat. Architectural tools, in principle, are smaller in size and low in strength. Due to their sophistication, traditional encryption algorithms are often computationally ineffective and require multiple rounds of coding, effectively wasting restricted device resources. But a less complex algorithm will jeopardize required honesty. In this article we propose a lightweight coding algorithm called Stable IoT (SIT). It is a 64-bit block encryption that requires a 64-bit key to encrypt data. Low complexity coding symmetry algorithm called Stable Power with Affine Shift. The coding component can be implemented using a simple syntax consisting of only basic mathematical operations (AND, OR, XOR, XNOR, SHIFTING, SWAPPING). This will help reduce the encoder, as the more complex key expansion method is only done in decryption. The purpose of this document is to conduct a security review and evaluate the performance of the proposed algorithm. The design of the algorithm is a combination of emotion and a unified switch-switch network. Simulation testing reveals that the algorithm provides adequate protection in only five rounds of encryption. The algorithm is hardware-implemented with a low-cost 8-bit microcontroller and the effects of code size, memory consumption, and encryption / decoding execution cycles are contrasted with reference encryption algorithms. © 2020 IEEE.
A new framework for defect detection using hybird machine learning techniques
(Institute of Electrical and Electronics Engineers Inc., 2022) Mansour, Fatma Suleman; Ibrahim, Abdullahi Abdu
In this study, some logs obtained with the Firewall Device are classified using multiclass support vector machine (SVM) classifier optimized by grid search algorithm. The presented method was compared with various data mining techniques. In addition, these learning algorithms were compared using four measures: Accuracy, Precision, Recall, and F-measure. In this paper, we propose the use of an automatic ICA-SVM to solve the defect problem in the computer network. It is the first automatic ICA to be used to reduce the size of input data. Then, the output of the ICA is connected to classifiers. SVM categorizes the attributes into three attacks (normal and abnormal). The proposed system showed results with an accuracy of 99.21% compared to some studies.
A novel energy-conscious threshold-based data Transmission routing protocol for wireless body area network (NEAT)
(Institute of Electrical and Electronics Engineers Inc., 2020) Ibrahim, Abdullahi Abdu; Salawudeen, Ahmed Tijani; Uçan, Osman Nuri; Okorie, Patrick Ubeh
In today's age of wireless communication, Wireless Body Area Network (WBAN) which is an extension of the conventional Wireless Sensor Network (WSN) is attracting immense interest in academia as well as industry. This is due to its importance in providing smart heath care service. One of the major research issues are Quality-of-Service (QoS) provision and energy efficiency improvement. Since sensor nodes are highly resource constrained in terms of battery and it is impractical to recharge and replace them, it is imperative to develop techniques/routing protocols or other solutions in other to augment the battery life. For that reason, NEAT routing algorithm which is an improvement on RE-ATTMPT and CEMob protocols is proposed in this paper. NEAT prioritize data into low-emergency, high-emergency and regular-data. Unlike similar protocols, NEAT ignores the communication of regular-data and transmit high-emergency data via direct communication and low-emergency data is compared with the formerly sensed low-emergency data and if it is different, it is transmitted, otherwise it is not transmitted thus leading to significant energy saving. Simulation results obtained by MATLAB prove that NEAT protocol outperforms RE-ATTMPT and CEMob in terms of network lifetime and throughput. © 2020 IEEE.
A printed reconfigurable monopole antenna based on a novel metamaterial structures for 5G applications
(2023) Al-Hadeethi, Saba T.; Elwi, Taha A.; Ibrahim, Abdullahi Abdu
A novel antenna structure is constructed from cascading multi-stage metamaterial (MTM) unit cells-based printed monopole antenna for 5G mobile communication networks. The proposed antenna is constructed from a printed conductive trace that fetches four MTM unit cells through four T-Resonators (TR) structures. Such a combination is introduced to enhance the antenna gain-bandwidth products at sub-6GHz bands after exiting the antenna with a coplanar waveguide (CPW) feed. The antenna circuitry is fabricated by etching a copper layer that is mounted on Taconic RF-43 substrate. Therefore, the proposed antenna occupies an effective area of 51 × 24 mm2. The proposed antenna provides an acceptable matching impedance with S11 ≤ -10 dB at 3.7 GHz, 4.6 GHz, 5.2 GHz, and 5.9 GHz. The antenna radiation patterns are evaluated at the frequency bands of interest with a gain average of 9.1-11.6 dBi. Later, to control the antenna performance, four optical switches based on LDR resistors are applied to control the antenna gain at 5.85 GHz, which is found to vary from 2 dBi to 11.6 dBi after varying the value of the LDR resistance from 700 Ω to 0 Ω, in descending manner. It is found that the proposed antenna provides an acceptable bit error rate (BER) with varying the antenna gain in a very acceptable manner in comparison to the ideal performance. Finally, the proposed antenna is fabricated to be tested experimentally in in free space and in close to the human body for portable applications.
A robust hybrid control model implementation for autonomous vehicles
(Institute of Electrical and Electronics Engineers Inc., 2024) Al-Jumaili, Mustafa Hamid; Özok, Yasa Ekşioğlu; Ibrahim, Abdullahi Abdu; Bayat, Oğuz
This work presents a robust control strategy for controlling autonomous vehicles under various conditions. This approach makes use of two controllers to guarantee excellent performance and a few faults when the car is traveling. Model Predictive and Stanley based controller (MPS) is the name of the new control system. This combines the functionality of a Stanley controller with a model predictive controller. The suggested approach tries to address these issues and provides a high-performance control system. Utilizing the finest aspects of both controllers and attempting to improve the other, this hybrid approach to integrating two well-known controllers provides advantages. The MPS is put to the test on straight and curvy roads in a variety of scenarios for both path-following and vehicle control. This controller has demonstrated excellent performance and adaptability to handle various autonomous driving conditions. When the findings are compared to earlier controller kinds, the suggested system performs better.
A survey on privacy and policy aspects of blockchain technology
(IEEE, 2022) Mahmood, Mohammed Thakir; Uçan, Osman Nuri; Ibrahim, Abdullahi Abdu
From financial transactions to digital voting systems, identity management, and asset monitoring, blockchain technology is increasingly being developed for use in a wide range of applications. The problem of security and privacy in the blockchain ecosystem, which is now a hot topic in the blockchain community, is discussed in this study. The survey’s goal was to investigate this issue by considering several sorts of assaults on the blockchain network in relation to the algorithms offered. Following a preliminary literature assessment, it appears that some attention has been paid to the first use case; however the second use case, to the best of my knowledge, deserves more attention when blockchain is used to investigate it. However, due to the subsequent government mandated secrecy around the implementation of DES, and the distrust of the academic community because of this, a movement was spawned that put a premium on individual privacy and decentralized control. This movement brought together the top minds in encryption and spawned the technology we know of as blockchain today. This survey paper also explores the genesis of encryption, its early adoption, and the government meddling which eventually spawned a movement which gave birth to the ideas behind blockchain. It also closes with a demonstration of blockchain technology used in a novel way to refactor the traditional design paradigms of databases.
Advancing medical imaging: detecting polypharmacy and adverse drug effects with Graph Convolutional Networks (GCN)
(2024) Dara, Omer Nabeel; Ibrahim, Abdullahi Abdu; Mohammed, Tareq Abed
Polypharmacy involves an individual using many medications at the same time and is a frequent healthcare technique used to treat complex medical disorders. Nevertheless, it also presents substantial risks of negative medication responses and interactions. Identifying and addressing adverse effects caused by polypharmacy is crucial to ensure patient safety and improve healthcare results. This paper introduces a new method using Graph Convolutional Networks (GCN) to identify polypharmacy side effects. Our strategy involves developing a medicine interaction graph in which edges signify drug-drug intuitive predicated on pharmacological properties and hubs symbolize drugs. GCN is a well-suited profound learning procedure for graph-based representations of social information. It can be used to anticipate the probability of medicate unfavorable impacts and to memorize important representations of sedate intuitive. Tests were conducted on a huge dataset of patients' pharmaceutical records commented on with watched medicate unfavorable impacts in arrange to approve our strategy. Execution of the GCN show, which was prepared on a subset of this dataset, was evaluated through a disarray framework. The perplexity network shows the precision with which the show categories occasions. Our discoveries demonstrate empowering advance within the recognizable proof of antagonistic responses related with polypharmaceuticals. For cardiovascular system target drugs, GCN technique achieved an accuracy of 94.12%, precision of 86.56%, F1-Score of 88.56%, AUC of 89.74% and recall of 87.92%. For respiratory system target drugs, GCN technique achieved an accuracy of 93.38%, precision of 85.64%, F1-Score of 89.79%, AUC of 91.85% and recall of 86.35%. And for nervous system target drugs, GCN technique achieved an accuracy of 95.27%, precision of 88.36%, F1-Score of 86.49%, AUC of 88.83% and recall of 84.73%. This research provides a significant contribution to pharmacovigilance by proposing a data-driven method to detect and reduce polypharmacy side effects, thereby increasing patient safety and healthcare decision-making.
An analysis system of fault diagnosis and classification in electrical energy system distribution networks based on convolutional neural network
(Institute of Electrical and Electronics Engineers Inc., 2023) Kadhum, Saja Jawad Kadhum; Ibrahim, Abdullahi Abdu
One of the primary causes contributing to the disruption of dependability and the termination of energy supply is the frequency of faults in distribution networks, making solar energy one of the most dependable renewable energy sources. Consequently, effective and rapid problem detection and prediction in distribution networks are critical for enhancing overall dependability, boosting customer happiness, and optimizing electrical energy efficiency. Rapid improvements in communication and automation technologies for distribution power networks allow a distributed generation (D.G.) protection coordination and reclosing strategy based on information exchange. Several artificial intelligence (A.I.) methodologies are used in modern classification, defect detection, and optimization for solar photovoltaic (P. V.) panels. Many algorithms are used to convert solar energy into electricity. During our investigation, we began searching for possible faults in the power system. The grid is a hybrid system that uses fossil fuels and solar energy to create electricity. The CNN approach was utilized and trained using a dataset comprising the highest and lowest voltage values that could be detected in a particular electrical network.
An improved dual-sink architecture with a modified media access control protocol, energy-aware, and quality-of-service guaranteed routing algorithms for wireless body area network
(SAGE Publications Ltd, 2022) Ibrahim, Abdullahi Abdu
To reduce frequent sensor recharging and replacement due to resource constraint, it becomes imperative to increase the management of energy and network’s quality of service. To this end, this article provides a new wireless body sensor network architecture with two sink nodes and multiple energy management and quality-of-service algorithms. The first algorithm is the normal data avoidance algorithm that is responsible for decreasing the energy usage by avoiding the transmission of normal data. Duplicate data avoidance algorithm avoid transmitting duplicate data thus saving the bandwidth and battery life. Past knowledge-based weighted routing algorithm oversees taking the ideal direction to transmit information, hence improving quality of service. Furthermore, sleep scheduling is integrated to further improve the battery life. In addition, in our proposed model, linear programming which is based on mathematical models was used to model the network lifetime maximization and continuous data transmission minimization. Through simulation in Castalia-based OMNeT++ demonstrates that our proposed work outperforms the works of quasi-sleep-preempt-supported with regard to network lifetime and delay with 50% and 30% improvement, respectively, moreover, it improves the work of critical data with respect to packet drop and throughput with 30% and 75% improvement, respectively.
Analysis the performance of AODV and DSDV routing protocols in different packets size by using the Flow monitor in NS3.19
(Institute of Electrical and Electronics Engineers Inc., 2020) Al-Rifai, Sufyan Sabah; Ibrahim, Abdullahi Abdu; Shantaf, Ahmed Muhi
The Mobile ad hoc networks are the networks that working without infastructure to coomuncation between the nodes in the networks and each node in this network as receiver and router in the same time to transsfer the packets from the soures to the destination. the mobillty of nodes are differents depend on the dynamic of the networks. in the paper we will evaluated the two protocols AODV and DSDV in three seniro then analysis the results with differents packet size in NS-3.19 © 2020 IEEE.
Analyzing and detecting the de-authentication attack by creating an automated scanner using Scapy
(Auricle Global Society of Education and Research, 2023) Al-Nuaimi, Mustafa Abdulkareem Salman; Ibrahim, Abdullahi Abdu
With the rapid spread of internet technologies around the world, the number of people that are using theinternet is increasing enormously in the last 10 years. with the increase in the number of people that are using the internet and the increase in the devices that depend on the internet such as computers, tablets, and mobile phones are raised the challenges of internet security against hackers who can steal sensitive information and exploits personal data. In this paper, we’re focusing on the home security threads and one of its famous attacks called the De-authentication attacks. The de-authentication frame is one of the Management frames that is transmitted between the AP and the connected devices and it can be used by attackers to apply a Dos attack and deny the devices from connecting to the network. In this paper. We will analyze the normal de-authentication frame and compare it with the attacking de-authentication frames to create an automated Scanner to identify whether it’s an attack, or it's a normal frame transmitted between AP and its connected devices, or vice versa. I would like to express my sincere gratitude to all those who have contributed to the successful completion of this research paper. Firstly, I would like to thank my supervisor Asst. Prof. Dr. Abdullahi Abdu IBRAHIM for his constant guidance, invaluable support, and insightful feedback throughout the research process. His expertise and advice have been instrumental in shaping the direction of this study. Finally, I would like to thank my family for their support, patience, and valuable suggestions. Their constant encouragement and motivation have helped me to overcome various challenges and obstacles encountered during my study.
Automation of traditional networks : a mini-review
(Institute of Electrical and Electronics Engineers Inc., 2024) Altalebi, Omar; Ibrahim, Abdullahi Abdu
In an era characterized by the fast advancement of networks, the challenge of effectively monitoring and regulating network devices has also escalated. Undoubtedly, the increase in the number of devices has resulted in elevated expenses, heightened vulnerability to human mistakes, extended timeframes for implementing modifications to these network devices, and a larger workforce. This work offers an in-depth analysis of conventional networks and their automation. Furthermore, including the statistical analysis conducted within this domain, the prospective trajectory of network automation, the imperatives necessitating its automation, and the inherent constraints associated with these networks. moreover, provides an analysis of the proposed gaps and solutions for these networks, which involve the automation of conventional networks. As well as it's included highlighting their advantages, disadvantages and examines prior research that has focused on the automation of conventional networks. The work aims to encompass a wide range of models that automate such networks. Additional ideas and recommendations for future research endeavors are provided as part of this work's exhaustive analysis of the findings.
Building an electronic health portal with an e-health application to communicate with patients
(Altınbaş Üniversitesi, 2022) Fazea, Ali Al; Ibrahim, Abdullahi Abdu
Patients have better access to their healthcare records and resources thanks to e-health portals. We create and deploy an e-health portal that efficiently integrates a variety of background medical services. The most difficult aspect of implementing such a system is ensuring that essential security criteria are met, such as patient data confidentiality, diagnostic outcome accuracy, and healthcare service availability. In this study, the design and implementation of a common electronic health records system, which various clinicians and patients can access, is presented depending on the RBAC access control. We focused on creating a patient- specific password through PHP programming functions. It is also possible, on this portal, to establish effective communication between the doctor and the patient, such as booking appointment electronically. Moreover, doctors can use the system to communicate urgent reports regarding the spread of newly discovered pandemics such as Coronavirus. System testing and evaluation are also offered.
Classification of epileptic seizure features from scalp electrical measurements using KNN and SVM based on fourier transform
(American Institute of Physics Inc., 2022) Al-Azzawi, Athar Hussein Ali; Al-Jumaili, Saif; Ibrahim, Abdullahi Abdu; Duru, Adil Deniz
Epilepsy classification techniques are one of the areas that are still under searching till now as long as there is no specific method for detection seizures. The brain consists of more than 100 billion nerves that generate electrical activity. These activities are recorded using an Electroencephalogram (EEG) by electrodes attached to the scalp. EEG is considered a big footstep in the medical and technical field where it allows the detection of brain disorders. However, this paper aims to identify the most efficient classification algorithm for classifying EEG signals of epileptic seizures. Therefore, we applied two classification techniques namely Support Vector Machine (SVM) and k-Nearest Neighbors (KNN), which rely on the features extracted from the data by the Fast Fourier Transform (FFT) method. The results show SVM obtained the highest accuracy value compared to KNN, accurate scores were 99.5% and 99%, respectively.
Classify bird species audio by augment convolutional neural network
(Institute of Electrical and Electronics Engineers Inc., 2022) Jasim, Hasan Abdullah; Ahmed, Saadaldeen R.; Ibrahim, Abdullahi Abdu; Duru, Adil Deniz
Using convolutional neural networks, this thesis aims to create a system for fully automated identification of bird species based on spectrogram images. Spectrogram analysis is more difficult when trying to make an advance identification of a bird species. On a publicly available dataset of 8000 audio examples, we've begun by analyzing the challenges of bird species detection, segmentation, and classification to achieve our goal. It has been determined also that deep learning-based technique CNN with Fully convolutional learning calls for easier results because it eliminates the possible future modelling error caused by an imprecise knowledge of bird species and works well on coding in cohesion with the spectral analysis kernel using the librosa library. We have concluded. After obtaining the dataset from the open-source repository, it is then processed locally. For training, testing, and validation we used a subset of the dataset of 8000 sound samples. We offered a method relying on a CNN reset learned that proved to be very quick and optimum because it was first needing the spectrogram analytic kernel to learn what to class in bird species, and then it gets the system trained on features extracted. In a novel 9-step implementation, a bird species spectrogram can be detected from an audio sample. There was a loss of less than 0.0063, and the conditioning workouts accuracy is 0.9895 for the system, 0.9 as precision, and training and validation use 50 epochs in system.
Clean medical data and predict heart disease
(Institute of Electrical and Electronics Engineers Inc., 2020) Alkhafaji, Mohammed Jasim A.; Aljuboori, Abbas Fadhil; Ibrahim, Abdullahi Abdu
The enormous data provided by the health care environment needs many important and powerful tools for analyzing and extracting data and accessing useful knowledge. Many researchers have been interested in applying many statistical tools as well as many different data mining tools in order to improve an analysis process and extract data from a different data set. The only thing that proves the success and robustness of data mining tool is accurate diagnosis of the disease. According to the (WHO), the biggest cause of death in the last ten years or so in this vast world is heart disease. The statistical exploration tools that researchers use are tools that help decision-makers in health care to predict and diagnose heart disease. The tools used in the diagnostic process for heart disease have been thoroughly tested in order to demonstrate sufficient and acceptable accuracy. A set of patient data divided into 665 records was used, of which 300 were for males, with 365 for females, with 10 different related characteristics. The decision-making department still suffers from a lack of performance and decision-making. Our paper aims to process data in different ways before the process of accessing knowledge to make the appropriate decision through expectations of classification analysis and then using techniques to extract data with acceptable accuracy. Our goal proposed in this paper is to purify the data before the disease prediction process to get the best possible prediction and compare the results with the results of a group of previous researchers to reach an accurate diagnosis and prediction. The second part of our goal is to compare between different technologies on different data sets such as decision tree technology and the second technique is Bayesian classification technology and the last technology is neural networks and the results were (98.85%, 98.16%, 91.31%), respectively. In the end, we hope to obtain acceptable results with high accuracy in the future, enhance clinical diagnosis, and promote appropriate decision-making for early treatment specialists. © 2020 IEEE.
Creating a smart medical bed with IoT technology for safe transportation of patients with highly infectious diseases
(Institute of Electrical and Electronics Engineers Inc., 2023) Hadi, Haneen Abdulhussein Hadi; Ibrahim, Abdullahi Abdu
The world has gone through a very dangerous circumstance that led to the closure of major countries and the closure of all life facilities, and this circumstance is the spread of the Corona pandemic (Covid-19), which has affected a very large number of people Therefore, the first team to deal with this disease was the medical staff, including doctors, nurses, and hospital workers, and this disease was spread by contact. Therefore, in the years of Corona, we lost very large numbers of doctors, nurses and workers inside hospitals, and therefore it was necessary to provide or make something that helps doctors and nurses in their work and are not exposed to this disease. After thinking we made a robot in the form of a medical bed, whose mission is to transfer the patient with this disease or any highly contagious disease from the hospital emergency entrance to the quarantine room. This was in my previous research. In the current research, we will develop this robot by adding a sensor that measures the percentage of oxygen in the patient's blood I added a sensor that measures the patient's temperature and sends it to the specialist doctor, and with this important process, we will ensure that the disease is not transmitted from the patient to the medical staff, and therefore we will preserve the lives of hundreds, but thousands of medical staff around the world. This robot is not affected by emotions, fatigue or external influences, as it will walk on a line that we previously set from the emergency entrance to the quarantine room in an automatic manner without stopping, and in the event that an object or anything is intercepted, its movement will switch from automatic to manual driving. After the manufacture of this robot, we do not need human intervention in order to transmit any infected with this virus.

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