Conferences Links
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Paper Review Claims |
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Paper ID |
Title |
Abstract |
Congress |
Claim |
| 1 |
68.Cnf-1206 |
Design of Array Inverted F Antenna for IOT
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This work analyzes the performance and designs a rectangular Microstrip patch F antenna. 5.9 GHz is the antenna's resonance frequency range, making it suitable for Internet of Things (IOT) applications. Simulation software for this work was Computer Simulation Technology (CST) software. A rectangular Inverted F coplanar antenna array structure was used in the antenna's design. The bandwidth, gain, and return on loss of these antennas were evaluated to determine their respective performances. The main findings of this study shown that, in comparison to a conventional antenna, the optimized array-shaped antenna increased bandwidth, gain, and return on loss. Furthermore, the improved antenna attained an operating frequency of 5.9 GHz, making it appropriate for Internet of Things applications. |
GC-ElecEng 2026 |
Claim |
| 2 |
46.Cnf-1207 |
Design and analysis of off -grid PV/diesel system for small scale factory located in Wadi Rum, Jordan using Homer software |
The fuel prices are increasing nowadays, causing a burden on the power system. From this, diesel generators that are still in use, must be hybridized with renewable energy (RE) to levelized the overall projects costs. This study aims to schedule an operation of a hybrid system photovoltaic (PV)/ diesel in a remote area which located in Wadi Rum district in Jordan to serve factory load in addition to obtain the best economical system combination that can serve the needs of a continuous electrical load without any interruption. Based on the results of the analysis using HOMER software, the configuration in the system will be a combination between PV modules, and diesel generator. The hybrid system has met the target for electricity production which is documented in upcoming sections. |
GC-ElecEng 2026 |
Claim |
| 3 |
68.Cnf-1208 |
A Blockchain Framework for Enhancing Selection of Optimal Parameters at NB-IOT |
By 2030, wireless communications will have connected over thirty billion devices. Narrowband Internet of Things (NB-IOT) technology has grown in popularity in response to the rapid growth of the internet of things (IOT) sector. The main aim of this study is to supply overall survey of the design modifications transported in the NB-IoT standardization along with comprehensive study evolutions according to popular companies in many countries such as: Telia, Elisa, Orange, Telecom Italia, Telstra, Vodafone, On the other hand, because there is a lot of work in NB-IOT on optimizing parameters or improving optimization methods, Consequently, this work presents a Blockchain architecture that may be used to enhance security, authentication, and efficient data access while maintaining data integrity. We describe the optimization parameters for physical channel and signal transmission and reception. Using the Physical Downlink Control Channel, we devise an adaptation scheme for 200 KHz bandwidth in NB-IOT networks (PDCCH). Finally, we want to locate the following: optimal parameters: Number of frames, Doppler frequency and Diversity performance. |
GC-ElecEng 2026 |
Claim |
| 4 |
0.Cnf-669 |
#41-Proposed Approach to Analyzing Quantum Noise using Operant Learning to Detect Invisible Threats Inside Industrial Control Systems |
The security of Industrial Control Systems (ICS) is a cornerstone of our collective efforts to protect cyberspace. In this research, an innovative approach to enhance the security of the industrial control system from modern and invisible cyber threats that traditional approach fail to detect. The proposed approach is based on the process of analyzing quantum noise resulting from industrial control systems while applying active learning algorithms to continuously improve the accuracy of the detection process. This research aim to develop a framework that enhances safety in critical infrastructure systems such as power plants and water networks. Experiments demonstrate the superiority of this approach over a range of conventional approaches in terms of robustness of accuracy and efficiency in detection, contributing and aiding the process of enhancing cyber resilience of industrial systems. |
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Claim |
| 5 |
99.Cnf-889 |
#42-Design and Implementation of a Digitally Controlled AC-DC Synchronous Buck Converter |
This paper presents the design, simulation, and implementation of a digitally controlled synchronous buck-type AC-DC converter using the STM32G491RE microcontroller. The converter steps down an AC input of 110–285 V into a regulated DC output of up to 16.8 V, optimized for charging applications. Digital voltage mode control was implemented using PI and Type-III compensators, modeled in SmartCtrl and verified in PSIM. A custom PCB integrating voltage/current sensors, EMI filtering, and gate drivers was fabricated. The converter maintained less than 3% output ripple under variable conditions, proving the stability and efficiency of the digital control. |
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Claim |
| 6 |
0.Cnf-896 |
#44-Analyzing Recurring Patterns in IPv6 Vulnerabilities to Identify Architecturally Weak Points |
This research focuses on analyzing security vulnerabilities associated with the Internet Protocol version 6 (IPv6) protocol, as documented in security databases such as the National Vulnerability Database (NVD) between 2023 and 2025, with the objective of identifying common patterns among them. The study involved collecting and documenting real-world vulnerabilities identified as Common Vulnerabilities and Exposures (CVEs), followed by classification based on the type of vulnerability, their Common Vulnerability Scoring System (CVSS) severity scores, the affected protocol components, and recurring weaknesses. Subsequently, the behavior of these vulnerabilities was examined to uncover repetitive patterns within the IPv6 architecture that are prone to attacks, aiming to highlight these areas for future remediation efforts. |
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Claim |
| 7 |
0.Cnf-905 |
#45-Feature Selection Using EVORA: An Elite Vector-based Optimization Algorithm with Reweighted Adaptation |
The increasing availability of high-dimensional datasets in modern applications poses significant challenges related to computational cost, overfitting, and model interpretability. Feature Selection (FS) is critical in reducing these issues by identifying the most relevant subset of features. Given the combinatorial nature of FS, metaheuristic algorithms have emerged as effective solutions. This study proposes a novel binary population-based metaheuristic, Elite Vector-based Optimization with Reweighted Adaptation (EVORA), explicitly designed for FS tasks. EVORA employs a probability vector to guide the generation of candidate solutions. Iteratively updates this vector by learning from elite-performing individuals, whose contributions are reweighted based on their normalized fitness. Before introducing EVORA’s methodology and theoretical formulation, a comparative review of twelve widely used metaheuristic algorithms for feature selection is conducted. Moreover, EVORA is evaluated on five diverse benchmark datasets: BCW, Sonar, Madelon, RELATHE, and BASEHOCK, using a K-Nearest Neighbors (KNN) classifier as the wrapper model. Experimental results demonstrate that EVORA achieves the highest or secondhighest classification accuracy across all datasets, with standout performance on Madelon (88.77%), BASEHOCK (92.15%), and Sonar (97.94%). Furthermore, EVORA consistently selects
significantly fewer features, achieving up to 86% reduction on high-dimensional datasets while exhibiting strong stability with the lowest standard deviation values. These findings underscore EVORA’s robustness, efficiency, and effectiveness as an FS method, making it a compelling tool for high-dimensional machine learning problems. |
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Claim |
| 8 |
99.Cnf-906 |
#46-Empowering Emotion Recognition with Generative AI: Classifying VAE-Augmented EEG Spectrograms Using Deep Learning |
This work presents a deep learning framework for EEG (Electroencephalogram)-based emotion recognition using spectrograms and generative augmentation. Raw EEG signals from the SEED-V dataset are converted into 2D spectrograms, then augmented using a Variational Autoencoder (VAE) with a 128-dimensional latent space. In addition, we have added a Graphical User Interface (GUI) that helps users adjust VAE parameters such as noise level and number of generations. Augmented data is used to fine-tune a pretrained ResNet50 model, achieving 90% accuracy. Results show that combining VAE-based augmentation with transfer learning significantly improves emotion classification performance from EEG data. |
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Claim |
| 9 |
0.Cnf-914 |
#47-HomoRoboNet: Deep Learning for Recognition of Humans vs Robot Face Images |
This paper presents HomoRoboNet, a deep learning-based system for distinguishing between real human faces and AI-generated (robotic) faces. With the rapid advancement of Generative Adversarial Networks (GANs), artificial images are becoming increasingly realistic, posing significant ethical and security challenges. To overcome such challenges, our approach uses Convolutional Neural Networks (CNNs) to detect subtle distortions and artifacts in AI-generated images that are usually imperceptible to the human eye. The model was trained on two datasets: the CelebA dataset of real human faces and the Thispersondoesnotexist.10k dataset of artificial faces. The experimental results show high classification accuracy, with a test accuracy of 93%. This work contributes to the ongoing efforts to fight digital fraud and misinformation by providing a robust tool for detecting AI-generated conten. |
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Claim |
| 10 |
.Cnf-938 |
#48-Race Strategy Optimization in Formula One Using Machine Learning and MATLAB Simulation |
Formula One (F1) racing is a mix of athletic precision and technological sophistication. With the rapid advancement of telemetry systems and real-time data communication, machine learning (ML) has become an essential element in optimizing race strategies. This paper investigates the integration of ML techniques with MATLAB-based simulation tools to enhance decision-making during competitive racing scenarios. Specifically, it focuses on predicting tire degradation—a dominant force in pit stop planning—through linear regression models trained on historical race data. The simulation results, tested with performance metrics including Mean Absolute Error (MAE) and R-squared (R²), show that even slight machine learning (ML) models are able to produce amazing tactical benefit by adding on point data and appropriate preprocessing. This approach bridges the gap between theoretical algorithmic models and real motorsport application, and hints at some new race intelligence engineering directions. |
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Claim |
| 11 |
99.Cnf-947 |
#49-Utilization of Sludge for Clean Energy Production and Biodegradable Plastics |
This research paper investigates sustainable methods for converting biological sludge from wastewater treatment plants into renewable energy and biodegradable materials. Separating the sludge and determining which organic components are appropriate for treatment is the first step. The process of anaerobic digestion, in which microorganisms break down organic matter without oxygen and produce methane gas, is being investigated. In order to help the plant become more energy independent and less reliant on fossil fuels, this methane is subsequently used to power fuel cells. Following digestion, the leftover sludge goes through microbial synthesis, where organic matter is transformed into biodegradable plastics, an environmentally friendly substitute for traditional plastics that aids in the fight against plastic pollution worldwide. Composting is also used to improve soil fertility by turning any leftover organic material into high-quality compost. Utilizing sludge from the first and second categories—which contain organic materials that decompose naturally—is the main goal of the study. Energy recovery, waste recycling, and the creation of biodegradable products are all combined in this integrated approach to provide a thorough sludge management plan. Overall, the study emphasizes how this system supports the circular economy, lessens its impact on the environment, and encourages sustainability in wastewater treatment. |
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Claim |
| 12 |
99.Cnf-985 |
#50-Toward Smarter Spacecraft: A Review of AI-Driven Autonomous Landing Systems |
The integration of autonomous systems and onboard artificial intelligence is transforming the landscape of extraterrestrial missions by enhancing landing accuracy and reducing operational risks. This paper presents a comprehensive review of how technologies such as vision-based hazard detection, sensor fusion, edge computing, and onboard AI are addressing the complexities of planetary exploration. Through the evaluation of real-time autonomous decision-making systems—like ALHAT, Φ-Sat, and IPEX—this study highlights the role of autonomy in enabling spacecraft to operate independently in unpredictable environments. Key benefits include improved data efficiency, minimized latency, and higher mission resilience. The paper also discusses the challenges of power constraints, data standardization, and fault detection integration, proposing pathways for future development in autonomous spacecraft architecture. These advancements signify a critical step toward sustainable and intelligent space exploration. |
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Claim |
| 13 |
99.Cnf-1005 |
#51-Real-Time Football Match Analysis: Leveraging YOLO for Enhanced Object Detection and Possession Tracking |
This paper presents a novel, real-time computer vision system for comprehensive football match analysis, focusing on the accurate detection and tracking of players, referees, and the ball. Leveraging advanced YOLO-based models, this research conducted a rigorous comparative evaluation of YOLOv8x and YOLOv12x models, marking the first documented application and performance comparison of YOLOv12x in football analytics. Our findings show YOLOv8x outperforms YOLOv12x in detection accuracy (F1-score), despite YOLOv12x’s higher efficiency in terms of fewer parameters and lower inference time. To enhance ball detection, we integrated a Kalman filter, significantly boosting its F1-score. The system also precisely calculates ball possession based on player-to-ball proximity. Tested on both local Jordanian and diverse global football matches, the proposed system offers substantial potential for improving sports broadcasting and advanced analytics, providing deeper insights into game dynamics and player performance. |
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Claim |
| 14 |
99.Cnf-1024 |
#53-User Engagement Prediction from Large-Scale Web Logs Using Efficient Feature Engineering and Gradient Boosting |
Our study explores a practical approach to predicting user engagement from large-scale web interaction data, using logs provided by the RecSys 2025 Challenge. Faced with the challenge of processing over 150 million records on limited hardware, we developed a memory-efficient, batch-wise processing framework that enabled the extraction of 39 meaningful behavioral features. These features capture key aspects of user activity, including frequency, timing, and variability. Using data from more than 850,000 users, we trained and evaluated Random Forest, XGBoost, and LightGBM models. XGBoost achieved the best performance, with an F1 score of 0.6978 and an accuracy of 0.7423, followed closely by LightGBM. Our findings highlight the effectiveness of carefully engineered temporal features and tree-based models in user engagement prediction, even in resource-constrained environments. This work offers a scalable and accessible solution for large-scale behavioral modeling and provides a strong foundation for incorporating deep learning methods in future research. |
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Claim |
| 15 |
99.Cnf-1023 |
#52-TechArt: Bridging Engineering and Visual Storytelling |
This paper outlines TechArt, an project aimed at developing interactive installations that merge engineering principles with visual arts to tell stories about how technology changed. The project seeks to create hands-on activities that highlight how technology affects people, fostering a deeper public understanding of engineering concepts through artistic expression. |
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Claim |
| 16 |
99.Cnf-1065 |
#59-IOTA-Based Dynamic DNS |
Recent research on DNS usability and security has identified ongoing challenges, many of which have been addressed through decentralized approaches. This paper presents a leaderless, peer-to-peer DNS system that replaces traditional domain names with user-generated public keys. Although prior studies have explored the feasibility of peer-to peer DNS in Internet of Things environments, our work offers a practical implementation tailored to the constraints of such systems. Using IOTA’s Fast Probabilistic Consensus algorithm, we address key challenges related to scalability and low transaction throughput. Additionally, our use of the Proof-of-Connection protocol enables a trustless architecture. Our results indicate that a decentralized DNS can operate securely without central authority or complex consensus mech anisms, both of which are often unsuitable for IoT. Simulations show that our Proof-of-Connection protocol achieves IP valida tion in a quick and lightweight fashion. |
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Claim |
| 17 |
99.Cnf-1080 |
#60-Unmanned Traffic Management System (UTMs) for Jordan : A Federated Architecture for Middle Eastern Drones Airspace Integration |
The rapid expansion of Unmanned Aircraft Systems (UAS) across various sectors, including inspection, surveying, monitoring, and delivery, has necessitated the development of a UAS Traffic Management (UTM) system. This paper explores the challenges and opportunities associated with UTM, focusing on its global adoption, key services, architecture, and the integration of communication, hardware, and software components. We highlight the case for Jordan as an example of how UTM can enhance airspace safety, economic growth, and innovation. |
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Claim |
| 18 |
99.Cnf-1081 |
#61-Linguistic Fingerprints: Fusing Stylometric Analysis and BERT for Robust Fake News Detection |
Fake news is a major online problem. While computer models like BERT are good at understanding text meaning, they often ignore writing style, which can be a key clue. This study tested if combining writing style analysis with BERT could improve fake news detection. We created 10 style features, like sentence length and punctuation use, and combined them with BERT's features from a large Kaggle dataset. Our hybrid LightGBM model was very effective, reaching 87.0% accuracy. Most importantly, it correctly identified 89% of fake news articles, showing a high recall for this critical task. In contrast, a BERT-only model performed poorly, which proves our approach works well. This research shows that an author's writing style is a powerful signal. Combining it with text meaning helps create better and more robust tools to fight digital misinformation. |
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Claim |
| 19 |
99.Cnf-1085 |
#62-Multi-Objective Chrnobly Disaster Optimizer |
One of the most challenging optimization problems in the real world is multi-objective optimization. Addressing these problems requires optimization techniques that can effectively handle the trade-off between objectives while ensuring both convergence and diversity in the solution set. Therefore, effective multi-objective algorithms should be developed to solve such problems. The Chornobyl disaster optimizer (CDO) is a new meta-heuristic optimization algorithm that was recently introduced to solve single-objective optimization problems. The algorithm was inspired by the Chernobyl disaster. The algorithm simulates the movement of the harmful particles that are released after the nuclear reactor core explosion in Chornobyl. In comparison with other algorithms, CDO has proven its ability to significantly achieve good outcomes. This motivated us to conduct a study aimed at optimizing the CDO to handle multi-objective optimization problems (MOOP) and achieve better outcomes for these kinds of problems. Specifically, this study introduces an enhanced version of the CDO for addressing multi-objective optimization problems. In the proposed algorithm, the crowding distance was employed to obtain non-dominated solutions in the population, which will maintain the diversity between the obtained solutions. Additionally, to improve the algorithm's exploration ability, an archive has been used for storing non-dominated solutions. To increase the efficiency of the algorithm, a polynomial mutation was applied to maintain the diversity of solutions. Moreover, the sine-cosine algorithm was integrated with the proposed algorithm to enhance the exploration and exploitation strategy. The performance of the suggested method was assessed by applying it on three sets of unconstrained multi-objective benchmark problems, that include ZDT, UF, and DTLZ benchmark problems. The outcomes were compared with four well-known multi-objective algorithms, which are Speed-constrained Multi-Objective Particle Swarm Optimization (SMPSO), Generalized Differential Evolution 3 (GDE3), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and A Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D). We utilized the GD, IGD, HV performance metrics to compare the proposed algorithm with other algorithms. The experimental results show that the multi-objective Chornobyl disaster optimizer (MOCDO) has a superior performance compared to other algorithms. |
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Claim |
| 20 |
99.Cnf-1090 |
#63-Astronomy Access for Visually Impaired Individuals |
Astronomy is a field that could be challenging for visually impaired people in both education and professional fields. That is because it relies heavily on visual data and the need to analyze graphs and images. However, technological advancements in artificial intelligence, 3-D printing, and sonification have led to improvements in accessibility. This paper examines existing efforts by organization, and highlights important technologies and methods to provide more accessibility. |
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Claim |
| 21 |
99.Cnf-1054 |
#58-Engineering a Low-Cost Rotary Collector for Enhanced Electrospun Nanofiber Orientation in Biomedical Applications |
The current study demonstrates effective low-cost rotary drum collector system for receiving the electrospun product in a typical biomedical electrospinning process. The main goal in this work was to design a low-cost functional device that could produce oriented nanofibers with potential usage for biomedical purposes. The collector system was made of CFR-PLA and had a drum diameter of 50.7mm and surface area of 185 cm². A 12V DC motor driven drum achieves rotation speeds of up to 12,000 RPM, while precise speed control is easily programmed using an adjustable controller and infrared sensor. The assembly was fixed on a 3D-printed base encouraging accessibility and modularity.
A 2% (w/v) polylactic acid (PLA) solution was prepared in chloroform and electrospun with a homemade syringe pump and a low-cost high voltage power supply (HVPS) under specific flow and voltage parameters. Aluminum foil-assisted conductive surface of the rotating drum facilitated efficient gathering of nanofiber under electric field. A scanning electron microscopy (SEM) examination of the nanofibers prepared under optimal conditions showed that relatively uniform morphology and an average fiber diameter of ~ 587 nm were achieved, which is competitive with the performance of existing systems. This design succeeds to being a financially viable solution while being able to maintain the integrity wetting functionality of electrospinning technology in lab and research settings. |
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Claim |
| 22 |
99.Cnf-1140 |
#64-Cyclodextrin-Based Nanosponges in Drug Delivery: Overcoming Poor Solubility and Bioavailability Challenges |
Nanosponges are innovative, porous nanoparticles emerging as a revolutionary tool in drug delivery. Their unique three-dimensional structure, often built from polymers like cyclodextrin, allows them to encapsulate various substances. A primary advantage of this technology is achieving significant solubility enhancement for poorly soluble drugs, a major challenge that often limits bioavailability. By forming inclusion complexes and increasing the drug's effective surface area, nanosponges can improve dissolution rates, protect therapeutic agents from degradation, and enable controlled release. This paper provides an overview of nanosponge technology, focuses on the mechanisms for solubility enhancement, and discusses the key challenges limiting their application. |
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| 23 |
99.Cnf-96 |
#67-Multi-Sensor Fusion for Victim Detection: Integrating Thermal Imaging, UWB Radar, and GPR in Search and Rescue Operation |
This paper investigates the practical applications of thermal imaging, UWB radar, and GPR in the context of SAR operations. It presents an overview of each technology, explores their synergistic potential through sensor fusion, and identifies the technical and operational challenges associated with their deployment. By examining case studies and recent research, this work aims to provide insights into how integrated sensing systems can transform the effectiveness of search and rescue missions in complex and hazardous environments. |
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Claim |
| 24 |
38.Cnf-1210 |
Supervised Neural Network Based Intrusion Detection Systems |
The magnificent problem for today’s computer networks is the internet attacks which is growing rapidly. Therefore, applying security methods to prevent such attacks on computer network is very important. Network attacks are challenging because they are continuously changing their patterns and techniques. Machine learning approaches play vital roles in detecting, and preventing attacks in different types of computer networks. The security is a critical issue whereas the security mechanism may affect the way of detecting, analysing, and preventing attacks. Therefore, security methods must modify their techniques to deal with. We have proposed Supervised Neural Network Based Intrusion Detection System (SNN-based IDS) to address a critical and timely issue in computer networking - the escalating threat of internet attacks. As these attacks continue to evolve, finding effective methods to detect and prevent them is of paramount importance. this paper leverages advanced technology to tackle the complex task of intrusion detection. This demonstrates the practical application of cutting-edge methods in cybersecurity. The paper suggests that the proposed model has the potential to predict and mitigate new types of attacks, even those not present in the training dataset. This feature is crucial in a rapidly evolving threat landscape. Our SNN is trained by KDDCUP’99 dataset which include 14 attack types in the test data, with an overall number of 24 training attack types. Experiments results show that the proposed system is capable to identifying the attacks and classifying them with high accuracy and reliability, regardless of the dataset's nonlinearity, size, or incompleteness. |
GC-Technology 2026 |
Claim |
| 25 |
99.Cnf-117 |
#72-Real-Time Violence Detection for In-Vehicle Safety |
Public transportation faces tremendous safety challenges, including harassment and conflict between passengers and drivers. These problems put lives in danger and discourage people from using public transportation. Several studies and pilot projects have explored real-time surveillance and behavior analysis to mitigate these issues. The proposed approach aims to minimize the consequences caused by these actions, by integrating an intelligent monitoring system that predicts such events in real-time. Utilizing the revolution of Machine Learning (ML) and Deep Learning (DL) models to detect, predict, and respond to potential fights between people inside vehicles. Once a critical occurrence is detected, the system can issue immediate alerts to relevant parties such as law enforcement, while also storing event data for documentation and model improvements. The initial testing of the designed system has shown an overall accuracy of approximately 97% in correctly identifying various types of violence actions compared to other existing works. This solution improves safety in shared transportation environments. |
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| 26 |
99.Cnf-120 |
#73-Crises and Consequences on Water Resources from the Environmental and Social Impacts of Traditional and Renewable Power Plants: Consumption And Pollution |
This paper examines the impacts of energy production on water resources, whether from conventional or renewable sources, with a focus on water consumption and pollution. The study highlights the adoption of sustainable energy practices and the improvement in water management, considering societal participation `and impacts. |
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| 27 |
99.Cnf-122 |
#74-Design and Development of Al-Rhim Sat A 1U CubeSat Jordanian Platform for Wildlife Tracking Using LoRa-Based IoT Technology |
Several native animals from several species are risking extinction in Jordan due to hunting, trade and collection of wildlife. The Royal Society for the Conservation of Nature (RSCN) encounters challenges in monitoring and regulating conservatories. This work presents the design of a 1U CubeSat mission supporting wildlife conservation in Jordan through space-based LoRa communication. The satellite enables longrange, low-power data collection from LoRa-enabled ground sensor nodes deployed in remote reserves where terrestrial networks are unavailable. We detail the end-to-end system architecture, including the CubeSat platform and custom LoRa ground stations designed for local assembly by students. The mission aims to validate LoRa performance in low Earth orbit and provide a scalable, low-cost solution for wildlife monitoring |
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Claim |
| 28 |
99.Cnf-123 |
#75-Optimized design of 32-bit Pipelined MIPS based on Verilog implementation |
This report presents the design, implementation, and verification of a five-stage, 32-bit pipelined processor conforming to the MIPS instruction set architecture (ISA). By dividing instruction execution into distinct stages—Instruction Fetch (IF), Instruction Decode (ID), Execute (EX), Memory Access (MEM), and Write-Back (WB)—we achieve instruction-level parallelism that significantly increases throughput. Key features include a hazard detection unit that stalls the pipeline on load-use hazards, a forwarding unit resolving most data dependencies, and support for control-flow instructions (branches and jumps), including the jr instruction. Simulation in ModelSim confirms correct functional behavior across arithmetic, logical, memory, and control instructions. Performance analysis indicates an average CPI of approximately 1.2 on benchmark programs, compared to CPI≈1.0 for an ideal pipeline and CPI≈1.8 for a non-pipelined design. |
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| 29 |
99.Cnf-320 |
#1-Steganography as an Anti-Forensics Technique |
Steganography is a powerful anti-forensics technique that involves hiding information within digital media, such as images, in a way that conceals the existence of the data itself. This paper explores a range of image-based steganographic methods, from basic techniques like Least Significant Bit (LSB) insertion to more advanced approaches including Discrete Cosine Transform (DCT)-based embedding, adaptive steganography, and deep learning-based models. The study highlights how these methods function, their levels of detectability, and the tools used to implement them. It also discusses the forensic detection methods used to uncover such hidden data, emphasizing the ongoing battle between steganography developers and digital forensic analysts. By examining both the implementation and detection of steganography, this paper provides a comprehensive view of its role in digital antiforensics and its implications for cybersecurity and forensic investigation. |
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| 30 |
99.Cnf-322 |
#65-Engineering Sentient UI: Bridging Emotion, Behavior, and Context for Dynamic UI Adaptation |
Despite increasing interactivity in digital systems, most user interfaces remain fundamentally static, unable to reflect the emotional and behavioral richness of human interaction. This disconnect impairs usability, limits accessibility, and fails to support truly personalized experiences. While research in affective computing and adaptive systems has outlined promising theoretical models, few practical implementations have bridged emotion, behavior, and context in real-time interfaces. This paper presents Sentient UI, a modular adaptive UI framework engineered in Flutter that dynamically adjusts interface components based on emotion recognition, behavioral patterns, and environmental context. It operates entirely offline, preserving user privacy while leveraging clean architectural design to
ensure extensibility and developer control through configurable adaptation layers. Sentient UI is being developed as part of a software engineering graduation project and is scheduled for open-source release in February 2026. By turning complex multi-modal sensing into accessible APIs, it offers a scalable foundation for building emotion-aware, real-time adaptive interfaces across everyday applications. |
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| 31 |
99.Cnf-331 |
#8-Attention Deficit Hyperactivity Disorder Detection Using Machine Learning Techniques On Reduced Datasets |
Attention Deficit Hyperactivity Disorder (ADHD) is commonly identified during childhood and frequently persists into adulthood. Children with ADHD may find it challenging to focus, control impulsive actions (such as acting without thinking about the outcomes), or may exhibit excessive activity. There are three main types of ADHD: Inattentive presentation, hyperactive/impulsive presentation, and combined presentation. in this paper, two main objectives are considered. The first is to identify the best classifier that can detect ADHD among twenty-one different classifiers that represent six learning strategies. The second objective is to determine the most effective feature selection method for datasets related to ADHD. The analysis highlighted the effectiveness of SMO, Multilayer Perceptron, in detecting ADHD. Additionally, SymmetricalUncertAttributeEval and Principal Component Analysis methods demonstrated the best performance among the ten considered feature selection techniques evaluated in this paper. |
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| 32 |
99.Cnf-332 |
#29-Design and implementation of a real-time full digital substation using RTDS simulator and protection relay |
Real-time simulation technologies are playing an increasing role in power system studies, driving the adoption of Hardware-in-the-Loop (HIL) methodologies for protection testing and validation. In addition, the new generation of the relays (Intelligent Electronic Devices - IEDs) support communication through the IEC61850 protocol, the state-of-the-art digital substation protocol. This research aims to explore the potential of using the IEDs for advanced research by connecting the IEDs to the Real-time digital simulator (RTDS), and to investigate the importance of this scheme for academic research, and testing the protection and control techniques before applying them to real-life applications, for optimization and risk minimization. In this research the RTDS device was connected to the ABB REX640 IED, using IEC61850. The research illustrates the steps and the requirements of connecting the IED to the simulator, in a Full digital substation scheme, where the IED receives the voltage and current values data and the breaker position from the RTDS. Also, the RTDS receives the trip signal from the IED via GOOSE communication. The results demonstrate an accurate, valid and stable connection between the RTDS and the IED. The simulation was built to measure the communication time using instantaneous over current relay function in the REX640 IED. Where the communication time for detection and isolating a fault was less than 6 milliseconds from the point when the fault occurred until the breaker opened to isolate the fault. |
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| 33 |
99.Cnf-361 |
#10-Enhancing Virtual Screening for Cancer Therapeutics: A Machine Learning Approach for HDAC1 Inhibition Prediction |
Accurate prediction of compound bioactivity is a key component in accelerating drug discovery, particularly for high-value targets such as Histone Deacetylase 1 (HDAC1). As a critical epigenetic
regulator, HDAC1 is implicated in the progression of several cancers, making it a promising therapeutic target. However, traditional compound screening is often costly and time-intensive. This study presents a machine learning–based virtual screening framework designed to predict the inhibitory activity of compounds against HDAC1. A dataset of approximately 9,000 compounds was retrieved from the ChEMBL database, with 8,804 molecular descriptors generated using PaDEL-Descriptor. Bioactivity data, represented by IC50 values, were discretized into three classes:
active, intermediate, and inactive. Using the PyCaret framework, we developed and evaluated several supervised learning models, including Light Gradient Boosting Machine (LightGBM), XGBoost, AdaBoost, Decision Tree, and Random Forest. Among them, the Gradient Boosting Classifier achieved the highest performance, with an accuracy of 93.13% and a Cohen’s kappa score of 0.8654. These results highlight the ability of ensemble models to capture complex structure–activity relationships. The proposed pipeline is scalable, reproducible, and efficient, offering a practical solution for early-stage virtual screening and supporting the rapid identification of HDAC1-targeted drug candidates. |
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| 34 |
99.Cnf-410 |
#12-Double Negative Reconfigurable Multilayered Imaging Metamaterial Antenna |
This paper presents a new, multilevel, dynamic feed-direction-controllable, double-polarized, C-band operation metamaterial antenna, i.e., to mention one example here, for bone fracture diagnostic application. It is constructed using PIN diodes to ensure dynamic feed directionality, copper walls, and levels of image gain enhancement and accuracy control. The novelty encompasses the hexagonal resonator type, square spiral shape metamaterial unit cell, utilized in the rotated 45-degree 2⨉2 lattice to achieve independent polarization operation. Simulation studies ensured double-negative behavior, polarization controllability, and proper enhancement of the gain. It is found that the whole antennal system presents a promising low-cost, portable, and emission-free approach relative to conventional approaches to bone fracture diagnostics, with the potential of enhanced patient access and outcome, to be utilized in medical imaging applications. |
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Claim |
| 35 |
99.Cnf-421 |
#14-Smart Transformer Integration in Jordan: A Technological Leap in Power Distribution |
The Solid-State Transformer (SST) represents a transformative innovation in electrical power systems, offering substantial advantages over conventional transformers in terms of efficiency, compactness, and seamless integration with modern smart grids. Leveraging advanced power electronic converters, SSTs enable precise voltage regulation, bidirectional power flow, and enhanced interfacing with renewable energy sources, electric vehicles, and microgrids. These attributes contribute to improved energy efficiency, better power quality, and superior fault tolerance, making SSTs an ideal candidate for modernizing power distribution, especially in regions such as Jordan, where renewable integration and energy reform are rapidly advancing. This paper explores the technical principles behind SSTs, their operational benefits, and their compatibility with Jordan’s energy goals. A key contribution lies in reviewing control of SST , which eliminate the need for traditional mechanical tap changers. The study also discusses implementation challenges, expected benefits, and future directions for integrating SSTs into Jordan’s grid. |
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| 36 |
99.Cnf-433 |
#5-Graphene: An Atom Away from Success – A Comprehensive Survey |
Graphene is often described as a wonder material with the potential to revolutionize science, technology, and environmental solutions. However, more than a decade after its discovery, its transformative promises remain largely unrealized due to persistent challenges in production, scalability, and integration. This paper traces graphene’s journey from scientific breakthrough to stalled innovation, analyzes the gap between its potential and current applications, and examines how emerging designs—such as a graphene-based solar absorber—might offer a more sustainable and realistic path forward. |
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99.Cnf-498 |
#22-Comprehensive Analysis Regarding Imbalanced Class Distribution Problem: Best Classification Model and Best Feature Selection Technique |
Classification is one of the main tasks in machine learning that aim to classify data into pre-defined groups called classes. To classify data, we need to use classifier and determine the importance of features by using feature selection techniques, but the performance of techniques always varies depending on the type of data. Therefore, we will classify the data, specifically imbalanced data, to determine the best feature selection technique and the best classifier for this type of data in this paper. To achieve these goals, six datasets with different imbalanced class distribution ratio were used, and seven feature selection techniques and six classifiers were applied. According to the results of three evaluation metrics which are F1-score, MCC, the area under the ROC curve; it became clear that the best feature selection technique is Information Gain Ratio and the best classifier is Random Forest. |
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| 38 |
99.Cnf-531 |
#25-A Sensitive Analysis for Teacher Burnout Identification using Machine Learning Method: Jordan Case Study |
Accurate burnout prediction is vital for research and decision-making, especially as burnout—caused by prolonged emotional, physical, and mental stress—is common among teachers facing challenges like large class sizes, frequent classes, and salary concerns. This study surveyed 500 Jordanian teachers using the Maslach Burnout Inventory and applied a hybrid machine-learning model to predict burnout levels. The model’s performance was evaluated using Accuracy, R2, MAE, and RMSE metrics via Python. The findings highlight the importance of prediction in reducing burnout’s negative impact, enabling data-driven interventions to improve working conditions and reduce turnover. Key teacher factors—Academic Qualification, Marital Status, Age, Years of Experience, and Salary Scale—combined with Maslach Inventory questions, proved essential for accurately predicting burnout levels. |
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| 39 |
99.Cnf-558 |
#66-EvalUI: An AI-Driven Framework for Structural Assessment of UI Layouts |
While modern interfaces change quickly, traditional accessibility evaluation is still limited by its focus on markup-based analysis. This restricts effective assessments to completed visual implementations. EvalUI is a new computer vision framework which allows thorough UI layout evaluation through visual analysis. The approach combines several new techniques: adaptive thresholding for reliable component detection in different visual conditions, perceptual modeling for WCAG 2.1 compliant contrast assessment, and graph-based spatial analysis that captures both overall layout structures and individual component relationships. The system is gradio-based and uses visual saliency simulation to provide measurable insights into viewing patterns and the effectiveness of visual hierarchy. A thorough evaluation of two different mobile interfaces, a minimalist contact management screen and the informationpacked GOV.UK homepage shows consistent performance across design types. The framework delivers detailed visual diagnostics, such as accessibility overlays and saliency heatmaps, along with standard metrics for contrast compliance (AA/AAA), spatial balance, and hierarchy effectiveness. Importantly, EvalUI works without needing source code, special file formats, or training data. This makes it particularly useful during early design stages when accessibility feedback is most needed but often unavailable. The results highlight the system’s ability to reveal subtle yet significant accessibility problems while offering practical suggestions for improving layout, thus connecting aesthetic design with an inclusive user experience and layout design assessment. |
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| 40 |
99.Cnf-563 |
#30-Feasibility and Design of an Off-Grid Solar-Powered EV Charging Station at Al-Huson College, Jordan |
This paper presents a comprehensive feasibility study and preliminary design of an off-grid, solar-powered DC fast charging (DCFC) station at Al-Huson College in Jordan. The project addresses the growing demand for sustainable transportation and leverages Jordan’s high solar energy potential to design a self-sufficient electric vehicle (EV) charging infrastructure. The study integrates a multi-phase methodology involving solar irradiance assessment, load demand estimation, component selection, and system simulation using industry-standard tools. A financial viability assessment and environmental impact analysis are also included. Key outcomes demonstrate the potential of such infrastructure to reduce reliance on fossil fuels, cut greenhouse gas emissions, and provide economic benefits for the institution. The proposed system design utilizes fixed-tilt photovoltaic panels, battery energy storage, and five 160 kW DCFC units, ensuring high efficiency and reliability. This research contributes a scalable model for similar campuses and off-grid facilities aiming to transition to green mobility. |
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| 41 |
99.Cnf-565 |
#31-Exploring Embedded Systems Toolchains, Language Safety and Efficient Programming Techniques
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This paper explores embedded systems toolchains, and safety-critical programming. It covers the role of compilers, cross-compilation, and debugging tools in embedded development. Additionally, the paper examines safety standards such as MISRA C, and common bugs and optimization techniques for memory and performance in embedded systems .Additionally, the paper includes a case study on the application of these concepts in the development of infusion pumps. |
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| 42 |
99.Cnf-584 |
#35-CardioRisk: A Machine Learning-Based Approach to Heart Disease Risk Prediction and Diagnosis |
This research aims to address the issue of cardiovascular diseases in an AI manner using machine learning models for the early prediction of heart disease to both decrease it’s mortality rate and reduce the economical weight it puts on patients and hospitals. At first we will collected the data and used preprocessing techniques to properly set up the data to be used in machine learning models using techniques like (Random Forest Imputer, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), One-Hot Encoder, Min-Max Scalar) then we used multiple machine learning models like (Gradient Boosting, Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Decision Tree, Random Forest, K-nearest neighbor (KNN), Linear Regression, ) after that we used the most suitable evaluation metrics like (Accuracy, Percision, Recall, F-1 score, Receiver Operating Characteristic(ROC) and Area Under the Curve(AUC) score) to identify the model with the best results. Our best-performing model, XGBoost, achieved an F1-score of 0.960, demonstrating improved predictive performance over similar models reported in previous studies, which achieved lower precision and recall due to less rigorous preprocessing. This demonstrates the effectiveness of our preprocessing pipeline and model selection in enhancing predictive performance. We hope that by the end of the research we would have contributed to the early detection of heart disease increasing patient outcomes through early interventions and decreasing the economical toll on patients and health sectors by decreasing the need for severe medical care for patients. |
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| 43 |
99.Cnf-593 |
#36-AraGrader Automated Arabic Short Answer Grading System using NLP and AraBERT Transformer |
The AraGrader project describes an automated Arabic short-answer grading model that uses Natural Language Processing (NLP) and the AraBERT transformer concept. This model is intended to solve the complexity of Arabic’s diverse morphology, syntax, and dialectal variances, improving efficiency, consistency, and fairness in grading student responses. |
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| 44 |
99.Cnf-622 |
#38-Intracranial Hemorrhage Detection In CT Scans Using Deep Learning |
Intracranial hemorrhage detection (ICH) is a serious medical condition that necessitates a prompt and exhaustive medical diagnosis. This paper addresses the multilabel classification task of ICH based on the RSNA 2019 brain
computed tomography (CT) dataset. This paper presents a hybrid deep learning approach leveraging a ResNeXt-101 CNN for spatial feature extraction with a bidirectional LSTM network to capture temporal dependencies across slice sequences. The model achieved a log loss of 0.04604, demonstrating strong generalization to unseen data. To enhance model interpretability and mitigate user resistance, XAI techniques were implemented, providing visual explanations of the model’s predictions. Additionally, a webbased application was developed to facilitate seamless clinical adoption and real-time interaction with the AI system. |
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| 45 |
99.Cnf-641 |
#40-Impact of Fault Location on Transient Stability in Power Systems |
Transient stability is a key aspect of power system performance under large disturbances such as faults. The location of a fault on a transmission line significantly influences the system’s ability to maintain synchronism.[1] This paper investigates the effect of fault location on transient stability by analyzing the system behavior during pre-fault, faulted, and post-fault periods[2]. A Single-Machine Infinite Bus (SMIB) system is used to simulate three-phase faults at different positions along the transmission line. The resulting swing curves reveal that faults closer to the generator reduce the critical clearing time and increase the risk of instability |
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