This paper presents a developed algorithm for reliability sensitivity analysis of engineering networks. Reliability Modeling is proposed for the Iraqi Kurdistan Regional Power Network (IKRPN) using Symbolic Reliability function of the model. The written Pascal code for the developed algorithm finds efficiently path sets and cut sets of the model. Reliability and Unreliability indices are found. The sensitivity of these indices are found with respect to the variation of the network’s elements reliabilities
Wireless Multimedia Sensor Networks (WMSNs) are being extensively utilized in critical applications such as envi- ronmental monitoring, surveillance, and healthcare, where the reliable transmission of packets is indispensable for seamless network operation. To address this requirement, this work presents a pioneering Distributed Dynamic Coop- eration Protocol (DDCP) routing algorithm. The DDCP algorithm aims to enhance packet reliability in WMSNs by prioritizing reliable packet delivery, improving packet delivery rates, minimizing end-to-end delay, and optimizing energy consumption. To evaluate its performance, the proposed algorithm is compared against traditional routing protocols like Ad hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR), as well as proactive routing protocols such as Optimized Link State Routing (OLSR). By dynamically adjusting the transmission range and selecting optimal paths through cooperative interactions with neighboring nodes, the DDCP algorithm offers effective solutions. Extensive simulations and experiments conducted on a wireless multimedia sensor node testbed demonstrate the superior performance of the DDCP routing algorithm compared to AODV, DSR, and OLSR, in terms of packet delivery rate, end-to-end delay, and energy efficiency. The comprehensive evaluation of the DDCP algorithm against multiple routing protocols provides valuable insights into its effectiveness and efficiency in improving packet reliability within WMSNs. Furthermore, the scalability and applicability of the proposed DDCP algorithm for large-scale wireless multimedia sensor networks are confirmed. In summary, the DDCP algorithm exhibits significant potential to enhance the performance of WMSNs, making it a suitable choice for a wide range of applications that demand robust and reliable data transmission.
This work presents a wireless communication network (WCN) infrastructure for the smart grid based on the technology of Worldwide Interoperability for Microwave Access (WiMAX) to address the main real-time applications of the smart grid such as Wide Area Monitoring and Control (WAMC), video surveillance, and distributed energy resources (DER) to provide low cost, flexibility, and expansion. Such wireless networks suffer from two significant impairments. On one hand, the data of real- time applications should deliver to the control center under robust conditions in terms of reliability and latency where the packet loss is increased with the increment of the number of industrial clients and transmission frequency rate under the limited capacity of WiMAX base station (BS). This research suggests wireless edge computing using WiMAX servers to address reliability and availability. On the other hand, BSs and servers consume affected energy from the power grid. Therefore, the suggested WCN is enhanced by green self-powered based on solar energy to compensate for the expected consumption of energy. The model of the system is built using an analytical approach and OPNET modeler. The results indicated that the suggested WCN based on green WiMAX BS and green edge computing can handle the latency and data reliability of the smart grid applications successfully and with a self-powered supply. For instance, WCN offered latency below 20 msec and received data reliability up to 99.99% in the case of the heaviest application in terms of data.
Wind energy and its conversion is part of renewable energy resources as cheaper and cleaner energy today even though the initial cost varies from place to place. Most of the government sector always promotes renewable energy with a provision of subsidies as observed worldwide. Wind energy is an actual solution over costlier conventional energy sources. If it is not properly placed and the selection of turbine design is not up to the mark, then investments may require more time to acquire Net Profit Value called as NPV. This research work is focused on the development of mathematical models to optimize the turbine size and locations considering all constraints such as the distance between the turbines, hub height, and investment in internal road and substation cost. Particle-Swarm-Optimization is an intelligent tool to optimize turbine place and size. The database management system is selected as the appropriate data storage platform for before and after optimization simulation. Various plots and excel outputs of .net programming are addressed for the success of optimization algorithms for the purpose of wind turbine placement and WTG design is suggested to manage wind energy such that power system reliability has been improved and the same is monitored through the reliability indices.
Distributed Generation (DG) can help in reducing the cost of electricity to the costumer, relieve network congestion and provide environmentally friendly energy close to load centers. Its capacity is also scalable and it provides voltage support at distribution level. Hence, DG placement and penetration level is an important problem for both the utility and DG owner. The Optimal Power Flow (OPF) has been widely used for both the operation and planning of a power system. The OPF is also suited for deregulated environment. Four different objective functions are considered in this study: (1) Improvement voltage profile (2) minimization of active power loss (3) maximum capacity of conductors (4) maximization of reliability level. The site and size of DG units are assumed as design variables. The results are discussed and compared with those of traditional distribution planning and also with Imperialist competitive algorithm (ICA). Key words: Distributed generation, distribution network planning, multi-objective optimization, and Imperialist competitive algorithm.
The reliability of power system under fault susceptible environment has become major challenge for the power sector units. The injection of renewable power source has increased the complexity for distribution system and to deal with massive network, evolution of smart-grid has been enforced, which works in an automated fashion to improve overall reliability, efficiency and quality of the system. Proactive Self-healing is a critical feature of smart-grid. This paper tries to explain the concept sensing the occurrence of fault beforehand and providing possible solution for self-healing in smart grid. The fundamental base for incorporating afore discussed technology viz. understanding nature of fault, sources of fault and implementation of effective measuring techniques are enumerated in paper briefly. Support required in terms of technology is reviewed towards the end followed by a case study of practical implementation of self-healing control in a distribution system.
Development of distribution systems result in higher system losses and poor voltage regulation. Consequently, an efficient and effective distribution system has become more urgent and important. Hence proper selection of conductors in the distribution system is important as it determines the current density and the resistance of the line. This paper examines the use of different evolutionary algorithms, genetic algorithm (GA), to optimal branch conductor selection in planning radial distribution systems with the objective to minimize the overall cost of annual energy losses and depreciation on the cost of conductors and reliability in order to improve productivity. Furthermore, The Backward-Forward sweep iterative method was adopted to solve the radial load flow analysis. Simulations are carried out on 69-bus radial distribution network using GA approach in order to show the accuracy as well as the efficiency of the proposed solution technique.
Nowadays, multimedia communication has become very widespread and this requires it to be protected from attackers and transmitted securely for reliability. Encryption and decryption techniques are useful in providing effective security for speech signals to ensure that these signals are transmitted with secure data and prevent third parties or the public from reading private messages. Due to the rapid improvement in digital communications over the recent period up to the present, the security of voice data transmitted over various networks has been classified as a favored field of study in earlier years. The contributions to audio encryption are discussed in this review. This Comprehensive review mainly focuses on presenting several kinds of methods for audio encryption and decryption the analysis of these methods with their advantages and disadvantages have been investigated thoroughly. It will be classified into encryption based on traditional methods and encryption based on advanced chaotic systems. They are divided into two types, continuous-time system, and discrete-time system, and also classified based on the synchronization method and the implementation method. In the fields of information and communications security, system designers face many challenges in both cost, performance, and architecture design, Field Programmable gate arrays (FPGAs) provide an excellent balance between computational power and processing flexibility. In addition, encryption methods will be classified based on Chaos-based Pseudo Random Bit Generator, Fractional-order systems, and hybrid chaotic generator systems, which is an advantageous point for this review compared with previous ones. Audio algorithms are presented, discussed, and compared, highlighting important advantages and disadvantages. Audio signals have a large volume and a strong correlation between data samples. Therefore, if traditional cryptography systems are used to encrypt such huge data, they gain significant overhead. Standard symmetric encryption systems also have a small key-space, which makes them vulnerable to attacks. On the other hand, encryption by asymmetric algorithms is not ideal due to low processing speed and complexity. Therefore, great importance has been given to using chaotic theory to encode audio files. Therefore, when proposing an appropriate encryption method to ensure a high degree of security, the key space, which is the critical part of every encryption system, and the key sensitivity must be taken into account. The key sensitivity is related to the initial values and control variables of the chaotic system chosen as the audio encryption algorithm. In addition, the proposed algorithm should eliminate the problems of periodic windows, such as limited chaotic range and non-uniform distribution, and the quality of the recovered audio signal remains good, which confirms the convenience, reliability, and high security.
This paper principally advises a simple and reliable control for Static Synchronous Compensator (STATCOM) in a stand-alone wind driven self-excited induction generator power system. The control was proposed based on instantaneous P-Q theory. The advised control enjoys the merits of robustness, reliability and simplicity. The paper also proposes a dimensioning procedure for the STATCOM that involves advising an annotative analytical expression for sizing the DC-link capacitor. This procedure has the advantages of applicability for different reactive power compensators that depend on a separate DC-link in its operation. Comprehensive simulation results in Matlab environment were illustrated for corroborating the performance of the advised control under rigorous operating scenarios. The results show the feasibility, reliability and practicability of the proposed controller.
Cybersecurity awareness has a huge impact on individuals and an even bigger impact on firms, universities, and institutes to those individuals belong. Consequently, it is essential to explore and asses the factors affecting the awareness level of cybersecurity. More specifically this research study examines the impact of demographic features of individuals on cybersecurity awareness. The Studied literature’s limitations have been addressed and overcome in our research from the variability, and ambiguity aspects. A questionnaire was developed and responses were collected from 613 participants. Reliability and validity tests as well as correlations have been applied for the instruments and data employed in this study. Coefficients were calculated via multiple linear regression for the weights of each of the cybersecurity components. Data reliability test showed that Cronbach’s Alpha value of 0.707 for the used data which is acceptable for research purposes. Results analysis showed r-value for each of the questions is greater than the r table which was 0.07992. Examining the proposed hypotheses showed that there is a difference as the null hypothesis is rejected for one of the demographic features being tested namely, gender. While there is no significant difference when it comes to the other two factors, education level, and age. Using the weight for each of the components, password security, technical behavior, and social influence could provide a solid base for decision-makers to focus on and implement the available resources for gender-specific developments to raise the cybersecurity awareness level..
In medium voltage and high-power drive applications, pulse width modulation (PWM) techniques are widely used to achieve effective speed control of AC motors. In real-time, an industrial drive system requires reduced hardware complexity and low computation time. The reliability of the AC drive can be improved with the FPGA (field programmable gate array) hardware equipped with digital controllers. To improve the performance of AC drives, a new FPGA-based Wavect real-time prototype controller (Xilinx ZYNQ-7000 SoC) is used to verify the effectiveness of the controller. These advanced controllers are capable of reducing computation time and enhancing the drive performance in real- time applications. The comparative performance analysis is carried out for the most commonly used voltage source inverter (VSI)-based PWM techniques such as sinusoidal pulse width modulation (SPWM) and space vector pulse width modulation (SVPWM) for three-phase, two-level inverters. The comparative study shows the SVPWM technique utilizes DC bus voltage more effectively and produces less harmonic distortion in terms of higher output voltage, flexible control of output frequency, and reduced harmonic distortion at output voltage for motor control applications. The simulation and hardware results are verified and validated by using MATLAB/Simulink software and FPGA-based Wavect real-time controller respectively.
The development of renewable resources and the deregulation of the market have made forecasting energy demand more critical in recent years. Advanced intelligent models are created to ensure accurate power projections for several time horizons to address new difficulties. Intelligent forecasting algorithms are a fundamental component of smart grids and a powerful tool for reducing uncertainty in order to make more cost- and energy-efficient decisions about generation scheduling, system reliability and power optimization, and profitable smart grid operations. However, since many crucial tasks of power operators, such as load dispatch, rely on short-term forecasts, prediction accuracy in forecasting algorithms is highly desired. This essay suggests a model for estimating Denmark’s power use that can precisely forecast the month’s demand. In order to identify factors that may have an impact on the pattern of a number of unique qualities in the city direct consumption of electricity. The current paper also demonstrates how to use an ensemble deep learning technique and Random forest to dramatically increase prediction accuracy. In addition to their ensemble, we showed how well the individual Random forest performed.
Smart Microgrid (MG) effectively contributes to supporting the electrical power systems as a whole and reducing the burden on the utility grid by the use of unconventional energy generation resources, in addition to backup Diesel Generators (DGs) for reliability increasing. In this paper, potential had been done on day-ahead scheduling of diesel generators and reducing the energy cost reached to the consumers side to side with renewable energy resources, where economical energy and cost-effective MG has been used based on optimization agent called Energy Management System (EMS). Improved Particle Swarm Optimization (IPSO) technique has been used as an optimization method to reduce fuel consumption and obtain the lowest energy cost as well as achieving the best performance to the energy system. Three scenarios are adopted to prove the efficiency of the proposed method. The first scenario uses a 24 hour time horizon to investigate the performance of the model, the second scenario uses two DGs and the third scenario depends on a 48-hour time horizon to validating the performance. The superiority of the proposed method is illustrated by comparing it with PSO and simulation results show using the proposed method can reducing the fuel demand and the energy cost by satisfying the user’s preference.
The reliability and feasibility of optical coherent communication system are strongly conditioned by laser phase noise and fluctuations of the state of polarization (SOP) of the optical field at the output of conventional single mode fiber. The double frequency parameter shift keying (DFPSK) system has been proposed in the literature as an efficient scheme that allows compensation of both effects by sending a reference channel that is suitably frequency shifted by using polarization modulation. This paper presents a comprehensive theoretical analysis for the performance of this system in the presence of dichroism which is introduced when the transmission channel has polarization dependent losses or amplifications. The results indicate that the performance of DFPSK system is affected by dichroism even in the low noise frequency regime.
This article introduces a novel Quantum-inspired Future Search Algorithm (QFSA), an innovative amalgamation of the classical Future Search Algorithm (FSA) and principles of quantum mechanics. The QFSA was formulated to enhance both exploration and exploitation capabilities, aiming to pinpoint the optimal solution more effectively. A rigorous evaluation was conducted using seven distinct benchmark functions, and the results were juxtaposed with five renowned algorithms from existing literature. Quantitatively, the QFSA outperformed its counterparts in a majority of the tested scenarios, indicating its superior efficiency and reliability. In the subsequent phase, the utility of QFSA was explored in the realm of fault detection in underground power cables. An Artificial Neural Network (ANN) was devised to identify and categorize faults in these cables. By integrating QFSA with ANN, a hybrid model, QFSA-ANN, was developed to optimize the network’s structure. The dataset, curated from MATLAB simulations, comprised diverse fault types at varying distances. The ANN structure had two primary units: one for fault location and another for detection. These units were fed with nine input parameters, including phase- currents and voltages, current and voltage values from zero sequences, and voltage angles from negative sequences. The optimal architecture of the ANN was determined by varying the number of neurons in the first and second hidden layers and fine-tuning the learning rate. To assert the efficacy of the QFSA-ANN model, it was tested under multiple fault conditions. A comparative analysis with established methods in the literature further accentuated its robustness in terms of fault detection and location accuracy. this research not only augments the field of search algorithms with QFSA but also showcases its practical application in enhancing fault detection in power distribution systems. Quantitative metrics, detailed in the main article, solidify the claim of QFSA-ANN’s superiority over conventional methods.
The demand for a secured web storage system is increasing daily for its reliability which ensures data privacy and confidentiality. The proposed paper aims to find the most secure ways to maintain integrity and protect privacy and security in healthcare management systems. The Advanced Encryption Standard (AES) algorithm is used to encrypt data transferred by providing a means to check the integrity of information transmitted and make it more immune to cyberattack techniques, this was implemented by using Keyed-Hash Message Authentication Code (HMAC) and Secured Hash Algorithm-256 (SHA-256). The risk of exposure to attackers can be avoided by using honeypot systems combined with Intrusion detection systems (IDSs) as a firewall system is not effective against such attacks alone. The experimental results evaluate the proposed security health information management system by comparing the performance of the encryption algorithm based on encryption time, memory and CPU usage, and entropy for different plaintext lengths. In addition, it can be seen that when changing the AES key size, more memory and time are required the longer the key size is used. The 128 bits AES key is therefore advised if the system must operate in hard real-time.
Real-time detection and recognition systems for vehicle license plates present a significant design and implementation challenge, arising from factors such as low image resolution, data noise, and various weather and lighting conditions.This study presents an efficient automated system for the identification and classification of vehicle license plates, utilizing deep learning techniques. The system is specifically designed for Iraqi vehicle license plates, adapting to various backgrounds, different font sizes, and non-standard formats. The proposed system has been designed to be integrated into an automated entrance gate security system. The system’s framework encompasses two primary phases: license plate detection (LPD) and character recognition (CR). The utilization of the advanced deep learning technique YOLOv4 has been implemented for both phases owing to its adeptness in real-time data processing and its remarkable precision in identifying diminutive entities like characters on license plates. In the LPD phase, the focal point is on the identification and isolation of license plates from images, whereas the CR phase is dedicated to the identification and extraction of characters from the identified license plates. A substantial dataset comprising Iraqi vehicle images captured under various lighting and weather circumstances has been amassed for the intention of both training and testing. The system attained a noteworthy accuracy level of 95.07%, coupled with an average processing time of 118.63 milliseconds for complete end-to-end operations on a specified dataset, thus highlighting its suitability for real-time applications. The results suggest that the proposed system has the capability to significantly enhance the efficiency and reliability of vehicle license plate recognition in various environmental conditions, thus making it suitable for implementation in security and traffic management contexts.
Given the role that pipelines play in transporting crude oil, which is considered the basis of the global economy and across different environments, hundreds of studies revolve around providing the necessary protection for it. Various technologies have been employed in this pursuit, differing in terms of cost, reliability, and efficiency, among other factors. Computer vision has emerged as a prominent technique in this field, albeit requiring a robust image-processing algorithm for spill detection. This study employs image segmentation techniques to enable the computer to interpret visual information and images effectively. The research focuses on detecting spills in oil pipes caused by leakage, utilizing images captured by a drone equipped with a Raspberry Pi and Pi camera. These images, along with their global positioning system (GPS) location, are transmitted to the base station using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol. At the base station, deep learning techniques, specifically Holistically-Nested Edge Detection (HED) and extreme inception (Xception) networks, are employed for image processing to identify contours. The proposed algorithm can detect multiple contours in the images. To pinpoint a contour with a black color, representative of an oil spill, the CIELAB color space (LAB) algorithm effectively removes shadow effects. If a contour is detected, its area and perimeter are calculated to determine whether it exceeds a certain threshold. The effectiveness of the proposed system was tested on Iraqi oil pipeline systems, demonstrating its capability to detect spills of different sizes.
In a human-robot interface, the prediction of motion, which is based on context information of a task, has the potential to improve the robustness and reliability of motion classification to control human-assisting manipulators. The objective of this work is to achieve better classification with multiple parameters using K-Nearest Neighbor (K-NN) for different movements of a prosthetic arm. The proposed structure is simulated using MATLAB Ver. R2009a, and satisfied results are obtained by comparing with the conventional recognition method using Artificial Neural Network (ANN). Results show the proposed K-NN technique achieved a uniformly good performance with respect to ANN in terms of time, which is important in recognition systems, and better accuracy in recognition when applied to lower Signal-to-Noise Ratio (SNR) signals.
Detecting pulmonary cancers at early stages is difficult but crucial for patient survival. Therefore, it is essential to develop an intelligent, autonomous, and accurate lung cancer detection system that shows great reliability compared to previous systems and research. In this study, we have developed an innovative lung cancer detection system known as the Hybrid Lung Cancer Stage Classifier and Diagnosis Model (Hybrid-LCSCDM). This system simplifies the complex task of diagnosing lung cancer by categorizing patients into three classes: normal, benign, and malignant, by analyzing computed tomography (CT) scans using a two-part approach: First, feature extraction is conducted using a pre-trained model called VGG-16 for detecting key features in lung CT scans indicative of cancer. Second, these features are then classified using a machine learning technique called XGBoost, which sorts the scans into three categories. A dataset, IQ-OTH/NCCD - Lung Cancer, is used to train and evaluate the proposed model to show its effectiveness. The dataset consists of the three aforementioned classes containing 1190 images. Our suggested strategy achieved an overall accuracy of 98.54%, while the classification precision among the three classes was 98.63%. Considering the accuracy, recall, and precision as well as the F1-score evaluation metrics, the results indicated that when using solely computed tomography scans, the proposed (Hybrid-LCSCDM) model outperforms all previously published models.
In this paper, explicit model predictive controller is applied to an inverted pendulum apparatus. Explicit solutions to constrained linear model predictive controller can be computed by solving multi-parametric quadratic programs. The solution is a piecewise affine function, which can be evaluated at each sample to obtain the optimal control law. The on-line computation effort is restricted to a table-lookup. This admits implementation on low cost hardware at high sampling frequencies in real-time systems with high reliability and low software complexity. This is useful for systems with limited power and CPU resources.
Path-planning is a crucial part of robotics, helping robots move through challenging places all by themselves. In this paper, we introduce an innovative approach to robot path-planning, a crucial aspect of robotics. This technique combines the power of Genetic Algorithm (GA) and Probabilistic Roadmap (PRM) to enhance efficiency and reliability. Our method takes into account challenges caused by moving obstacles, making it skilled at navigating complex environments. Through merging GA’s exploration abilities with PRM’s global planning strengths, our GA-PRM algorithm improves computational efficiency and finds optimal paths. To validate our approach, we conducted rigorous evaluations against well-known algorithms including A*, RRT, Genetic Algorithm, and PRM in simulated environments. The results were remarkable, with our GA-PRM algorithm outperforming existing methods, achieving an average path length of 25.6235 units and an average computational time of 0.6881 seconds, demonstrating its speed and effectiveness. Additionally, the paths generated were notably smoother, with an average value of 0.3133. These findings highlight the potential of the GA-PRM algorithm in real-world applications, especially in crucial sectors like healthcare, where efficient path-planning is essential. This research contributes significantly to the field of path-planning and offers valuable insights for the future design of autonomous robotic systems.
Experts and researchers in the field of information security have placed a high value on the security of image data in the last few years. They have presented several image encryption techniques that are more secure. To increase the security level of image encryption algorithms, this article offers an efficient diffusion approach for image encryption methods based on one- dimensional Logistic, three-dimensional Lorenz, DNA encoding and computing, and SHA-256. The encryption test demonstrates that the method has great security and reliability. This article, also, examines the security of encryption methods, such as secret key space analysis, key sensitivity test, histogram analysis, information entropy process, correlation examination, and differential attack. When the image encryption method described in this article is compared to several previous image encryption techniques, the encryption algorithm has higher information entropy and a lower correlation coefficient.
In this study, we propose a compact, tri-band microstrip patch antenna for 5G applications, operating at 28 GHz, 38 GHz, and 60 GHz frequency bands. Starting with a basic rectangular microstrip patch, modifications were made to achieve resonance in the target frequency bands and improve S11 performance, gain, and impedance bandwidth. An inset feed was employed to enhance antenna matching, and a π–shaped slot was incorporated into the radiating patch for better antenna characteristics. The design utilized a Rogers RT/Duroid-5880 substrate with a 0.508 mm thickness, a 2.2 dielectric constant, and a 0.0009 loss tangent. The final dimensions of the antenna are 8 x 8.5 x 0.508 mm3. The maximum S11 values obtained at the resonant frequencies of 27.9 GHz, 38.4 GHz, and 56 GHz are -15.4 dB, -18 dB, and -26.4 dB, respectively. The impedance bandwidths around these frequencies were 1.26 GHz (27.245 - 28.505), 1.08 GHz (37.775 - 38.855), and 12.015 GHz (51.725 - 63.74), respectively. The antenna gains at the resonant frequencies are 7.96 dBi, 6.82 dBi, and 7.93 dBi, respectively. Radiation efficiencies of 88%, 84%, and 90% were achieved at the resonant frequencies. However, it is observed that the radiation is maximum in the broadside direction at 28 GHz, although it peaks at −41o/41o and −30o/30o at 38 GHz and 56 GHz, respectively. Furthermore, the antenna design, simulations, and optimizations were carried out using HFSS, and the results were verified with CST. Both simulators showed a reasonable degree of consistency, confirming the effectiveness and reliability of the proposed antenna design.
Large disturbances in an induction generator-based wind system necessitate rapid compensation for the reactive power. This article addresses the application of Static Synchronous Compensator (STATCOM) in optimizing the performance of grid connected wind power system. The functionality of the static synchronous compensator in maintaining system stability and reliability during/post diverse severe disturbances is thoroughly investigated. A design procedure for STATCOM, particularly the capacitor in the DC side was advised.
Epilepsy, a neurological disorder characterized by recurring seizures, necessitates early and precise detection for effective management. Deep learning techniques have emerged as powerful tools for analyzing complex medical data, specifically electroencephalogram (EEG) signals, advancing epileptic detection. This review comprehensively presents cutting-edge methodologies in deep learning-based epileptic detection systems. Beginning with an overview of epilepsy’s fundamental concepts and their implications for individuals and healthcare are present. This review then delves into deep learning principles and their application in processing EEG signals. Diverse research papers to know the architectures—convolutional neural networks, recurrent neural networks, and hybrid models—are investigated, emphasizing their strengths and limitations in detecting epilepsy. Preprocessing techniques for improving EEG data quality and reliability, such as noise reduction, artifact removal, and feature extraction, are discussed. Present performance evaluation metrics in epileptic detection, such as accuracy, sensitivity, specificity, and area under the curve, are provided. This review anticipates future directions by highlighting challenges such as dataset size and diversity, model interpretability, and integration with clinical decision support systems. Finally, this review demonstrates how deep learning can improve the precision, efficiency, and accessibility of early epileptic diagnosis. This advancement allows for more timely interventions and personalized treatment plans, potentially revolutionizing epilepsy management.