International Journal of Computers and Informatics
The International Journal of Computers and Informatics (IJCI) is an international refereed scientific journal specializing in the fields of Computer science, software engineering, information systems, information technology, data science, and management information systems. It is published by the publishing house “Vision for Scientific Research and Publishing” located in London, United Kingdom, which is managed by academics with long scientific expertise. The journal is interested in publishing scientific research that has originality, modernity, and scientific methodology, which constitute a useful scientific addition, whether in Arabic or English. The research published in the journal is subject to an arbitration process by a group of specialized academic professors from many countries of the world. The journal is also responsible for indexing and archiving Arab scientific and knowledge production in major international scientific databases.
Vision for Scientific Research and Publishing
ALEIDF: An Adaptive Lightweight and Explainable Hybrid Deep Learning Framework for Intrusion Detection in Resource-Constrained IoT Networks
The proliferation of Internet of Things (IoT) networks in smart homes, healthcare, industrial systems, and critical infrastructure has greatly extended the attack surface, and the deep learning models that are best suited to detecting these attacks are challenging to deploy on resource-limited edge devices, difficult to trust because they are black-box, and difficult to maintain effectively because traffic and attack distributions are evolving over time. Lightweight, explainable, and adaptive intrusion detection have been researched as largely distinct lines of work so far. This paper proposes a unified framework (ALEIDF) which consolidates eight tightly coupled modules, namely: Adaptive Feature Evolution Module (AFE), Hybrid Deep Learning Engine (HDLE), Adaptive Threat Memory (ATM), Explainability Module (XM), Decision Engine (DE), Resource Optimization Module (ROM), and Online Learning Module (OLM). The ALEIDF is equipped with feature level-drift detection, as well as retraining triggers; externalizes threat knowledge into an episodic memory query; calibrates detection confidence against this memory; and scopes the generation of explanations towards the drift adapted feature subset, which address the accuracy-efficiency gap, efficiency-explainability gap, and adaptability-knowledge-retention gap pointed out in the recent IoT-IDS literature. In a cross-dataset test with UNSW-NB15 and X-IIoTID, ALEIDF achieves a macro F1 score of about 97.7%, fast inference latency of < 10ms on microcontroller-class hardware and about 40% faster recovery from simulated concept drift than federated-incremental baselines, while costing just 10% as much relative to explanation generation as full-feature SHAP. Having identified joint integration of efficiency, explainability and adaptability as an open field in recent surveys regarding IoT network security research, ALEIDF is well positioned to further this integration.
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An Innovative Numerical Comparison of Euler and Taylor Series Methods in Solving Differential Equations with Application to the Logistic Growth Equation
The objective of this work is to numerically compare the two numerical techniques for solving ordinary differential equations, namely the Euler method and the Taylor series method. The special focus of this numerical comparison will be on solving both the linear and nonlinear ordinary differential equations that can be modelled using the logistic growth model. The methodology employed in this study to achieve these objectives includes a detailed mathematical formulation of the problem followed by the numerical implementation of each method using the MATLAB computing environment and the determination of differences between the two methods with respect to their accuracy and computational efficiency. Additionally, a new hybrid method (the hybrid Euler-Taylor method), which uses a standard Euler approximation and applies a second order Taylor correction is also presented. This hybrid method provides an average performance between these two methods in terms of both accuracy and computational efficiency. The results show that while the Euler method is easy to implement, it is not very accurate, whereas the Taylor series method is very accurate but requires more computational effort. The hybrid Euler-Taylor method occupies the “sweet spot” between accuracy and computational efficiency.
Employing Visible Light Communication for Evolving Network Performance of Next Cellular Systems
A developed optical air interface for forthcoming short-range transmission in mobile networks, in which visible light communication (VLC) is utilized with orthogonal generalized frequency division multiplexing (OGFDM), is introduced, explored, and evaluated. The presented waveform, termed VLC-OGFDM, is used in a back-to-back optical wireless transceiver system that employs a high-frequency light carrier for optical modulation. The proposed design aims to overcome the channel bandwidth limitations of the waveform by maximizing the transmission bit rate in short-range optical wireless communication scenarios (indoor/outdoor) for future smart cities. Hence, although both the proposed VLC-OGFDM and the conventional OGFDM operate in the wireless domain, the VLC-OGFDM channel bit rate is significantly higher than that of OGFDM because it operates at a higher frequency (VLC) than radio frequency (RF). Besides, using optical rather than electrical modulation in wireless communications provides a high level of protection by avoiding interference with surrounding electrical signals. As such, the VLC-OGFDM waveform is a sophisticated version of the old-style RF-OGFDM, which enables the transmission of large amounts of data over short and safe distances. For electric-to-optic transformation and vice versa, the VLC-OGFDM requires auxiliary optical equipment such as a light-emitting diode (LED), an intensity modulator (IM), and a photodetector. The performance of the VLC-OGFDM system, including essential signal power, error level, and maximum transmission bit rate, is investigated relative to the electrical OGFDM. Ultimately, a MATLAB simulation based on the proposed system's parameters is used for performance validation.
SQLEM++: An Explainable Hybrid Learning Framework for SQL Query Cost Prediction and Optimization
While relational database systems can process billions of queries per day with SQL, their cost-based optimizers use hand-crafted heuristics and simplified cardinality estimators that can lead to large estimation errors. These errors creep up into the query plan selection, often leading to poor or even disastrous performance when the query is executed. In this paper, new framework for SQL query cost prediction and recommendation of optimize SQL query, named as SQLEM++ (SQL Query Learning and Embedding Model). SQLEM++ is a dual-encoder architecture consisting of a domain-adapted transformer (SQL-BERT) and a structural Graph Neural Network (GNN) over query Abstract Syntax Trees to encode queries in semantically rich representations. The framework also includes heterogenous feature extraction of join complexity, filter selectivity, index coverage, and schema-level statistics. A multi-task prediction module is used to estimate the execution cost along with providing the classification of query quality into actionable levels, using the asymmetric Huber loss. Further, an explainability layer, derived from explainability with SHAP attribution and attention roll-out, converts model predictions to interpretable optimization recommendations. On top of this, SQLEM++ is introduced as an adaptive extension that fuses a learned cost model with the traditional optimizers in a hybrid manner, an uncertainty aware decision algorithm for reliability control, and a reinforcement learning feedback loop for continuous self-improvement. The results of an extensive evaluation on TPC-H (scale factors 1–100), and TPC-DS show that SQLEM achieves an average speed-up of query execution by 3.24 times compared to the PostgreSQL 16 optimizer and a mean absolute prediction error reduction of 71.4%, with an R² of 0.94. Ablation studies validate that both semantic and structural encoders support each other.
Predicting Patients with Renal Failure using Neural Networks
Artificial neural network considered as most important in statistic and artificial intelligent that reflex important improvement for future prediction a series of time data from 1999 to 2018, which represent 20 series of time data represent renal failure patients men and women, bath. Results of the research reach predictions in three years represented in the following years (2019-2020-2021). Through results the predictions values have best used way from traditional used way in prediction previously. The documents loosed on researchers from Ibn Seena Hospital in Nenavah Governorate in renal failure.
An Intelligent Hybrid Data Mining Framework for Healthcare Fraud Detection Using Machine Learning and Association Rule Mining
Healthcare insurance fraud is one of the biggest healthcare challenges today and all over the world. The conventional fraud detection approaches are based on manual auditing and rule-based systems and tend to be ineffective in detecting fraudulent behaviors as they change over time. In this paper, Association Rule Mining (ARM), Random Forest (RF) and Isolation Forest (IF) algorithms are combined to provide a novel hybrid data mining for intelligent healthcare fraud detection. The novelty of the proposed approach is the fusion of supervised and unsupervised learning techniques as well as of the adaptive risk scoring, which will enhance the detection accuracy and reduce false positives. The framework uses an insurance claim data set from healthcare to identify any unusual patterns and transactions which could be suspicious. The experimental results show that the proposed hybrid model outperforms the conventional machine learning models in terms of accuracy, precision, recall, and F1-score. Under highly imbalanced datasets, the proposed framework achieved an accuracy of 97.2% and significantly enhanced the fraud detection performance. Research provides a scalable and explainable data mining solution appropriate for real-world health care systems.
Graph Structures Arising from Ideals in Commutative Rings
In the present paper, an algebraic-combinatorial approach is introduced to study ideal graphs associated with commutative rings, and standard constructions like Noetherian, Artinian, Local rings, and Principal Ideal Domains (PIDs) are specially studied. Intersection and co-intersection of proper ideals are used to build ideal graphs, and the study of graph invariant properties like chromatic number, clique number and symmetries of automorphisms can then be explored. The paper performs case studies in detail such as rings, e.g., rings with names like <!-- [if gte msEquation 12]><m:oMath><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>Z</m:r></m:e><m:sub><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>6</m:r></m:sub></m:sSub></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 4.0pt; mso-text-raise: -4.0pt; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> , <!-- [if gte msEquation 12]><m:oMath><m:sSub><m:sSubPr><m:ctrlPr></m:ctrlPr></m:sSubPr><m:e><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>Z</m:r></m:e><m:sub><m:r><m:rPr><m:scr m:val="roman"/><m:sty m:val="p"/></m:rPr>12</m:r></m:sub></m:sSub></m:oMath><![endif]--> <span style="font-size: 11.0pt; line-height: 107%; font-family: 'Calibri',sans-serif; mso-ascii-theme-font: minor-latin; mso-fareast-font-family: Calibri; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: Arial; mso-bidi-theme-font: minor-bidi; position: relative; top: 4.0pt; mso-text-raise: -4.0pt; mso-font-kerning: 0pt; mso-ligatures: none; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"> , local Galois rings, Artinian rings with finite decompositions of the rings to see how the structure and complexity of the graph formed as a result of the algebraic details of the rings that make the structure (the number of maximal ideals, chain divisibility, and lattice morphisms). One of the main contributions of this research is the establishment and demonstration of three new theorems, namely the Ideal Projection Symmetry Theorem which entails that the automorphism group of an ideal graph splits according to the product decomposition of the ring; the Ideal Graph Entanglement Principle, which introduces a new index that measures the hierarchical complexity of ideal graphs; and the Ideal Nesting Spectrum Theorem, relating the clique number of a graph to the length of intersecting chains of ideals. this survey of ring classes shows how these invariants and theoretical constructions The resulting popsicle graph model provides new avenues of ring structure classification, which aid in enhanced research in algebraic topology, structural graph theory, and computational ring studies.
Federated Learning-Based Secure Intrusion Detection Framework for Edge-IoT Communication Systems
The advent of Edge-Internet of Things (Edge-IoT) communication systems has created a huge cyber security problem with connected devices being heterogeneous, distributed and resource constrained. Centralized intrusion detection systems (IDSs) tend to be data-hungry, impose significant communications overhead, add latency, and have serious privacy concerns. However, due to these limitations, this paper suggests an Edge-IoT Communication Systems Federated Learning-Based Secure Intrusion Detection Framework which facilitates collaborative and secure detection of cyberattacks without sharing raw data from the network. The proposed system includes federated learning systems to enable local ID models training at edge nodes, and distributed edge intelligence to send only encrypted model parameters to a central coordinator. The architecture is built to be used in smart home, smart healthcare, and industrial IoT applications that allow for both the scalable and decentralized cybersecurity operations. It uses a set of deep learning-based IDS models like Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and hybrid autoencoder architectures to identify several cyber threats, including distributed denial of service (DDoS) attacks, botnets, spoofing attacks, ransomware traffic, and abnormal communication behavior. It also includes differential privacy and secure aggregation techniques to ensure privacy and security in local model updates from users, mitigating the risk of inference attacks and unauthorized access. Experimental analysis is conducted using CICIDS2017 dataset and the results show that the proposed federated framework outperforms the centralized IDS and traditional machine learning techniques in terms of high IDS accuracy, low false alarm rates, less communication overhead, and privacy preservation. The findings suggest that the proposed framework offers an efficient, scalable, and lightweight cybersecurity solution for next-generation Edge-IoT communication systems without compromising the information security and adaptive threat intelligence.
Small Cell Technology in Heterogeneous Wireless Mobile Networks of Future Smart Cities
In this paper, the use of intelligent heterogeneous mobile networks in congested smart cities, where macro, micro, pico, and femto cells are smartly monitored, is examined. The study demonstrates, from a physical-layer standpoint, the key benefits of deploying smart-driven small cells, including coverage area, transmission throughput, and resource consumption. The proposed design employs progressive intelligent software solutions to enable smart coordination among the installed base stations via the used waveform. By adopting both advanced cell technologies and smart applications, a developed communication system for future smart cities can be realized. Accordingly, the smart control of the presented scheme achieves lower battery usage, higher throughput, and wider transmission. Specifically, the offered solution is applied to crowded cities around the world, particularly the capitals and tourist cities, where millions of people visit each year. To verify the system performance of our advanced, smart, and effective solution, the transceiver systems at the explored locations were investigated before and after peak times using MATLAB.
Resilient Waveform Employing FPGA Configuration
Since the high-bit loading concept has attracted the attention of the wireless communication community, an important level of processing, like the digital modulation process, should be exploited efficiently. Thus, due to the challenging requirements of future mobile networks, particularly those related to big-data applications, the huge channel capacity of wireless communication represents a significant topic for the upcoming mobile technology. In this paper, the impact of utilizing the field programmable gate array (FPGA) for the developed waveform shape of the fifth generation (5G) mobile and beyond is demonstrated. The performance of transmission in terms of improved bit-rate is smoothly processed under the FPGA domination and in an electrical back-to-back wireless network system. By employing the elastic features of the FPGA, the advanced modulation formats are introduced herein as an effective solution for the physical layer (PHY) of the transmission system. As such, getting a higher bandwidth (BW) efficiency is softly obtained by adopting a flexible manipulation for the enhanced bit loading technique. Thus, in contrast to the conventional application- specific integrated circuit (ASIC) that is struggling to deal with an updated shape of modulation unless changing the whole infrastructure, the resilient platform of the FPGA can enhance the modulation process while keeping the existing hardware design. Therefore, by updating the installed software of the FPGA, the modulation level can be easily promoted from the low-level to the high-level modulation schemes.
Application- Specific Integrated Circuit (ASIC)
A Cost-Effective Framework for Transforming Conventional Microscopes into Intelligent Diagnostic Platforms using Systematic Multi-Field Acquisition Protocol and Multimodal LLMs
Urine and stool cultures under the microscope are a main method of diagnostic work, but manual analysis is not only dependent on experience of the examiner but also suffers visual fatigue and subjectivity. Although, automated systems are available, their cost is so high that they are not often integrated in resource-limited environments. In this paper,we proposes a MicroScan AI, a low-cost solution that will be used to convert a standard optical microscope into a smart platform capable of diagnostic tasks. Morphological reasoning is done by connecting a digital microscopic camera with a 0.5x reduction lens with multimodal Large Language Models (LLM), namely ChatGPT-4o, Gemini, and Claude, and conducting the reasoning through encrypted API channels. A counting-based aggregation methodology was used to counteract a 60% field-of-view (FOV) hardware-induced field limit by a structured five-field acquisition scheme (F1–F5) scheme and Systematic Multi-Field for image pre-processing protocol . The validation of the experiment was done using 20 clinical specimens and 200 diagnostic parameters against expert human opinion. The system had an agreement rate of 97% with a Cohen Kappa coefficient of 0.94 which signifies an almost perfect agreement. Examination of the 3-percent discrepancy indicated that the error occurred in adjoining semi-quantitative grading frequencies and not resultant categorical detection errors. These results make MicroScan AI a scalable clinical decision support system that improves diagnostic consistency and decreases operator dependency without necessitating the complete replacement of hardware.
Secure and Efficient Search over Encrypted Data using Blockchain Technology
The accelerated popularity of cloud infrastructure and storage has led to a plethora of privacy and security concerns about sensitive data. As a countermeasure, when outsourcing data is usually encrypted. On the other hand, regular encryption makes it impossible to search through that data making any real-world applications challenging. Searchable Symmetric Encryption (SSE) is an efficient building block that allows us to avoid cryptographic search over encrypted data without decryption. However, central SSE systems are subject to single point of failure and transparency. This paper seeks to accomplish secure, efficient and verifiable search over encrypted data by combining SSE with blockchain within a decentralized architecture. The methodology enables data integrity, privacy of queries and the possibility of complete query auditing through meticulous use of smart contracts and distributed ledgers. It implements AES-256-GCM data encryption, HMAC-SHA256 trapdoor generation, Proof of Work blockchain simulation and smart contract execution engine. Experimental results show that it generates indexes for 1,000 documents in less time than 265 ms and executes search in less than 48 Ms per query.
A Scientific Analysis of a Hierarchical Active Inference Model for Coordinated Multi-Point Beamforming
Coordinated multi-point (CoMP) transmission is viewed as an innovative solution for future generation wireless systems with the ability to meet rising data rate requests. CoMP requires coordination among BSs to facilitate either resource allocation or joint transmission. However, this coordination is challenging as BSs have dynamic interference patterns and the decisions of one BS impact the decisions made by other BSs [1]. Active inference is a broad theoretical framework based around the ideas of Bayesian statistics, free-energy minimization, and constructivism. This new worldview suggests that biological agents, not just humans but also animals, do not only make decisions and interpretations, but also adapt to and perfect the internal generative models, so as to explain their environments. While conditioning on multiple stimuli and responding to the world in light of them, agents develop an adaptation mechanism to cope with unpredictable changing environments. With respect to this system, generative models will be systematically represented hierarchically according to the model. The upper levels of this hierarchy are the abstract latent variables and although harder to estimate, yet provide important contextual meaning to the lower level variables, and the variables at these lower levels are able to make sense of perception and impact on the ground, possibly directly changing it (because they affect it). With this comprehensive framework, the agents have to learn constantly, growing and adapting according to the time they see and evolve to respond to the highly evolving world around them. Beamforming optimization per-BS for CoMP transmission can be interpreted as beamforming control. There exists a local generative model that accounts for channel observations over time for each BS. The local model states are dynamic with changing channel dynamics. Assuming the beamforming parameters, BS minimizes the free-energy cost of the generative model. The free-energy control law possesses a dualistic character with respect to an approach to minimizing channel-state estimation error while allowing for maximum throughput/energy efficiency by users.
Explainable Hybrid Machine Learning for Autonomous Optimization of Network QOS in Cyber Security
Cyber Security is a smart defence strategy that uses advanced artificial intelligence as a shield. Artificial Intelligence (AI) and Machine Learning (ML) enhance cyber security by enabling faster and more accurate threat detection, automated responses and adaptive defence system that learn from data. Network security on the other hand, involves protecting, monitoring and controlling the network infrastructure and the data transmitted across it. In this study predictive analysis was performed to classify network threats based on key performance metrics such as band width, latency response time and jitter. The network data set consisted of 1000 instances and 5 attributes to evaluate the classification performance; several data mining algorithms namely Naive Bayes, IBK, SMO, Logistic, and Random Tree were implemented using the WEKA data mining tool. The experimental results indicate that the Random Tree classified achieved the best performance compared to the other algorithms. The accuracy obtained by the five algorithms was as follows (Random Tree 99.8%) IBK (95.9%) Naive Bayes (94.8%) Logistic Regression (87.9%) and SMO (87.7%). These findings demonstrate that the Random Tree model provides superior predictive capability for network threat detection. The confusion matrix analysis indicates that the proposed model has a strong capability to correctly identify attack traffic. With minimal misclassification. The model also demonstrates reliable performance in distinguishing between warning and normal network states. These results confirm it effectiveness in detecting real time performance degradation and identifying potential network threats. The evaluation process includes measuring model accuracy to determine the effectiveness of the classification results. The findings indicate that machine learning based approaches significantly improve network monitoring performance and enhance cyber security decision making processes. These results demonstrate the importance of predictive models in identifying network anomalies and supporting proactive threat mitigation strategies.
Feasibility of Implementing a Trains Ticket Management System in Iraq Based on Global Experience
This study explores the feasibility of adopting a modern digital system for managing train tickets in Iraq, drawing on the experiences and best practices of leading countries in this field, such as the United States, Russia, and Turkey. The study employs a comparative analytical approach, supported by a conceptual system design. Through comparative analysis, it examines the gap between the current state of train management services in Iraq and more advanced international systems. The findings indicate several weaknesses, particularly in the digital ticketing infrastructure, data processing, and the system's level of digital integration. To address these issues, the study proposes a phased implementation plan, starting with simple digital services and evolving over time into a more integrated smart transportation system. Results also suggest that leveraging successful international practices, especially in areas such as e-ticketing, digital payment methods, and data utilization in operations, can improve efficiency and enhance passenger satisfaction. These improvements contribute to streamlining procedures, facilitating access to services, and supporting future integration with broader smart transportation solutions. Study concluded that Iraq has a real opportunity to develop a modern and efficient train ticketing system by leveraging international expertise that has achieved passenger satisfaction and significant economic gains, provided that this development is supported by sound infrastructure built according to international standards and principles, and by the gradual adoption of technology in line with international standards.
Emerging Architectures in Direct-Clip Optical Communication: A Comprehensive Review
The global demand for ultra-high-speed data transmission intensifies, Intensity-Modulation Direct-Detection (IM/DD) systems have recently turned as the pillar of short-reach optical networks like 5G/6G front haul, hyper-scale data-centres and Optical Wireless Communication (OWC/Li-Fi). Out of all the modulation candidates, Direct-Current Biased Optical Orthogonal Frequency Division Multiplexing (DCO-OFDM) is one of the most promising modulation formats due to the benefits of high spectral efficiency and immunity to the multipath fading and ISI due to the inherent nature of this format. Nonetheless, high DC bias currents must be used to prevent signal clipping in optical emitters (e.g., Light Emitting Diodes (LEDs) and Laser Diodes (LDs)) due to the large power fluctuations, which results in inefficient energy utilization and an accelerated deterioration of the component. In addition, if these peaks become larger than the small linear dynamic range of inexpensive hardware then they cause strong non-linear distortion, greatly increasing the Bit Error Rate (BER) while also limiting the modulation order possible (e.g. 64-QAM to 1024-QAM). This review totally examines the Direct Clip framework a further developed type of digital flag handling (DSP) that is utilized to limit these impediments. While typical clipping regards signal peaks as random errors, Direct Clip takes advantage of Iterative Clipping and Filtering (ICF) and Noise Shaping to actively push the signal envelope into the ideal domain of operation for hardware. This paper illustrates the trade-offs between computational complexity, spectral leakage, and fidelity through individual blocks to show how Direct-Clip architectures facilitate the utilization of low-resolution Digital-to-Analog Converters (DACs) and non-linear optical components for high-throughput applications. These findings confirm that Direct-Clip Optical-OFDM is much more than an incremental advance, but rather one of the core building blocks for sustainable, low-cost, high-capacity Optical connectivity at the next level of software-defined networks.
OpinionML: An Interpretable Machine Learning Framework for Opinion Mining on Social Networking Sites
In recent years, the dramatic increase in the volume of user-generated content on social media, making effective opinion analysis systems more essential than ever. Tasks such as sentiment analysis, aspect extraction, topic modeling, and fake review detection are often addressed separately, although they are closely related. This isolating limits the potential for leveraging shared information. In this paper, we present OpinionML, a machine learning framework designed to unify tasks into a single framework. The framework uses a common feature engineering pipeline that combines several feature types: TF-IDF, lexical, syn- tactic, behavioral, and topic-based. Topic information is extracted using latent Dirichlet allocation (LDA) and augmented to obtain additional contextual cues. In modeling, different problems require different approaches—so we use support vector machines, random forests, and conditional random fields, depending on suitability. The proposed framework evaluated on standard datasets, (SemEval- 2016, Yelp, Amazon, and Sentiment140). The results show that our approach demonstrates competitive performance compared to traditional machine learn- ing methods while remaining interpretable and computationally feasible, and can make opinion analysis systems more effective and flexible in practice.
Design thinking: its concept, stages, tools, and role in designing the learning experience
This study, titled “Design Thinking: Its Concept, Stages, Tools, and Role in Learning Experience Design,” explores Design Thinking as an innovative framework for addressing complex educational challenges and enhancing learning environments in alignment with Saudi Vision 2030. It examines the concept and five key stages of Design Thinking (empathize, define, ideate, prototype, and test) and highlights its role in Learning Experience Design (LXD). Using a descriptive-analytical approach based on a review of relevant literature and frameworks, the study found that Design Thinking promotes a learner-centered approach by prioritizing empathy and effectively develops 21st-century skills such as creativity, collaboration, and problem -solving. The findings also indicate that integrating Design Thinking with LXD supports the development of flexible, iterative learning models that improve learner engagement and outcomes. The study recommends training educators in Design Thinking tools and fostering an institutional culture of innovation.
Technical challenges of e-governance and ways to ensure successful digital transformation in the government sector
This study seeks to identify the combined impact of the dimensions of digital governance (technology infrastructure and operational processes, management and authority, accountability and transparency, and digital risk management) on digital transformation. The digital transformation era represents the "application of digital capabilities to processes, products, and assets" with the aim of "improving efficiency, enhancing customer value, managing risks, and unlocking new revenue opportunities." Similarly, digital transformation impacts not only life but also individual experiences. This study focuses on the mechanisms necessary to digitally transform the interaction between governments and citizens to ensure greater transparency, accountability, and integrity. It contributes to defining the current state of e-governance and digital maturity in government institutions.
This study explores the foresight of e-learning's future as a scientific vision aimed at avoiding past errors and constructing innovative educational models. The source examines global transformations triggered by the COVID-19 pandemic, which accelerated digital transformation and the adoption of advanced technologies such as Learning Management Systems (LMS) and adaptive learning. Furthermore, the study focuses on modern trends, including the integration of Artificial Intelligence (AI), Virtual Reality (VR), and mobile learning to personalize learner experiences and enhance educational quality. The findings underscore the significance of micro-credentials and flexible learning pathways in supporting lifelong learning and aligning education with labor market demands. Despite vast technological opportunities, the study identifies core challenges, most notably ensuring digital equity and justice, protecting privacy, and updating curricula to keep pace with rapid changes. The study concludes by recommending the support of innovative and institutional initiatives to ensure the future sustainability of digital education.
Performance Analysis and Quality of Service for Video Streaming in Vehicular Adhoc Networks
Although safety applications have been the main driving force behind the development of Vehicular Ad-hoc Networks (VANETs), non-safety applications can also greatly benefit vehicular networks. Video streaming services, among non-security applications, can offer appealing features to many users and draw a large number of users. However, the adoption of video services in vehicular networks is challenging due to the high mobility characteristics of VANETs and packet loss during communication blackouts. This research is unique in the field of software-defined networks that examined a video streaming service's effectiveness to investigate how well a video on demand service would work for users in vehicles on a highway. The packet loss caused by network reconfiguration during handoffs and its impact on the quality of the video transmitted have both been examined. With the help of SDN technology, we were able to maintain uninterrupted video streaming between vehicles in a vehicular ad hoc network.
E-Healthcare Adoption, User Satisfaction, and Clinical Effectiveness
Background: E-healthcare, telemedicine, and online health platforms are being increasingly employed to facilitate greater access to healthcare services. Nevertheless, there remains a need to generate real-world evidence for e-healthcare utilization, patient satisfaction, and outcomes from diverse geographic regions across the world. Objective: To identify the factors that determine e-healthcare utilization and patient satisfaction, as well as to determine if certain clinical outcomes from e-healthcare utilization are non-inferior to traditional healthcare, a mixed-methods approach was employed across diverse global regions. Methods: A total of 100 patient survey respondents, 167 patients for clinical outcomes, and 7 healthcare provider interviews were conducted over a period of 24 months in North America, Europe, and Asia Pacific. Quantitative methods employed multivariable logistic regression, non-inferiority propensity score matching, and survival analyses. Qualitative methods employed thematic content analysis. Results: Of the total survey respondents (n = 102), the mean age was 48.3 years (SD = 14.2), with a retention rate of 74.2% (n = 76) for 12 months or longer. Determinants for e-healthcare utilization were digital literacy (OR = 2.34; CI = 1.89, 2.91) and convenience (OR = 2.18; CI = 1.76, 2.70). The mean patient satisfaction score was 8.4 (SD = 1.2) out of a possible 10, with a major predictor for satisfaction being trust for the provider (β = 0.62; p < 0.001). Non-inferiority tests for clinical outcomes for e-healthcare utilization compared to traditional healthcare for diabetes, hypertension, and mental health showed non-inferiority for e-healthcare utilization. For diabetes, the differences in HbA1c for e-healthcare compared to traditional healthcare were -0.15% (CI -0.38% to +0.08%) with a non-inferiority margin of -0.5%. Hypertension control was achieved in 95.8% vs. 94.2% (p = 0.002), while improvement in depressive symptoms was by 7.2 vs. 6.8 on the PHQ-9 (p = 0.156). Acute care utilization was reduced by 23% for e-healthcare (38 per 1,000 vs. 49 per 1,000 (p < 0.001)). Three major challenges for e-healthcare utilization were training needs, interoperability, and reimbursement policy.
A Consideration of Potential Strategies for Patient Safety from Cyberattack in the Healthcare Domain
The rapidly rising convergence of digital solutions in healthcare has enhanced performance and patient care, but introduced numerous cybersecurity challenges. Cyberattacks of all types—including ransomware, phishing, and supply chain intrusions—have grown in frequency and severity, focusing clearly on sensitive health data and disrupting essential clinical services. These events may result in the delay of the treatment, and the Inaccuracy of care, and at times, even put lives of patients in danger. This review considers healthcare cyber threats and analyses the effects of these threats, whether direct or circumstantial, on patient safety. It proposes an integrated approach that includes technical, managerial, and governance measures proactively prevent, reduce, and recover from cyber events. Some of the primary measures include proper network partitioning, real-time monitoring of system threats, comprehensive training for employees, incident response strategies, and increased control from regulators. By shifting the approach to cybersecurity as a matter of patient safety, rather than just an IT problem, hospitals can improve their ability to counter cyber risks and sustain the delivery of safe and high-quality services. This review supports a multi-disciplinary strategy to incorporate cybersecurity into safety culture and operational strategy at the clinical level.
A Comparative Study of Artificial Neural Networks and Reinforcement Learning Models
Cyber-attacks have grown more sophisticated, posing critical threats to national infrastructure and data integrity. Deep learning methods, particularly Artificial Neural Networks (ANNs) and Reinforcement Learning (RL), offer promising capabilities in cyber threat detection and response. However, existing approaches face limitations in scalability, data sufficiency, and real-time adaptability. This study conducts a comparative analysis of ANNs and RL models to assess their effectiveness in analyzing cyber-attacks within the Saudi National Cybersecurity Authority. Using a descriptive analytical approach, data were gathered from 105 experts via questionnaires and 10 specialists through interviews. Results reveal that both models exhibit high performance in detection accuracy, adaptability, and operational efficiency. Nevertheless, challenges persist, including data dependency and interpretability. The study contributes by (1) evaluating the current implementation of ANNs and RL in Saudi cybersecurity infrastructure, (2) identifying practical and technical limitations of each model, and (3) recommending adaptive, hybrid strategies for enhanced cyber defense using deep learning.
A Noise-Aware Image Enhancement Framework Based on Joint Spatial–Frequency Analysis
Most image enhancement methods are developed based on a tacit assumption: noise is an undesirable artifact to be suppressed before or during enhancing. Be that as it may, this assumption does not hold for real-world noise, which has a structural characteristic of its own. It's not constant in space, nor does it present a spectrally random pattern; instead, it reflects uncertainties about local situations and signal reliability in collected data. Typical methods which treat noise as an interference instead of information often bring out over-enhancement or fake textures, missing original detail. In this paper, we provide a framework of noise-aware image enhancement that interprets signal disturbance as structural evidence rather than being an anomaly. The process then becomes redefined from intensification to making decisions on the basis of noise reliability. To implement this, a combined spatial-frequency domain analysis is employed to study noise behaviour both locally and globally. By fusing these complementary noise markers, we give selective enhancement: structures which are firmly based will be built up while enhancement is held back in zones heavily dominated by noise. In further development, this paper introduces a hybrid analytical-learning extension. A lightweight neural module learns the degree to which enhancement should be applied, while being explicitly conditioned on noise descriptors that are understandable. Experimental results show that the method proposed in this paper achieves stable and visually comprehensive enhancement under conditions of challenging noise: it maintains a high degree of fidelity to structure and does not add noise. This work will lead to a conceptual change in future enhancement design: constituted as an essential principle rather than some way of eliminating the only concerned factor.
Hardware Co-Simulation Based Deep Neural Networks for Liver Diseases Detection in MRI Images
Accurate characterization of focal liver lesions on multiphasic magnetic resonance imaging (MRI) is central to early detection and treatment planning; however, clinical deployment of deep learning remains constrained by compute cost, latency, and the need for rigorous verification of hardware implementations. This paper presents LiverNet-Q, a hardware-aware, multi-phase deep neural network for multi-class liver disease detection from MRI, coupled with an end-to-end hardware co-simulation workflow that validates functional equivalence between the software model and the synthesized register-transfer level (RTL) design. The proposed pipeline first localizes the liver with a lightweight U-Net trained using public liver MRI annotations, then classifies lesions into five clinically relevant categories (normal liver, hepatocellular carcinoma, hemangioma, focal nodular hyperplasia, and simple cyst) using attention-based phase fusion. To enable resource-efficient inference, LiverNet-Q is trained with quantization-aware training and deployed in INT8 precision. The accelerator is implemented with Vitis HLS using a streaming dataflow micro-architecture that targets an initiation interval of one for core convolution operators. Experiments on public benchmarks demonstrate that INT8 deployment preserves diagnostic performance with a small loss relative to FP32 while providing substantial speedups. Hardware co-simulation reports confirm cycle-accurate latency and throughput, supporting reproducible, deployment-ready evaluation.
Bayesian model for assessing cybersecurity risks based on Naive Bayes classifier
The research analyzed cybersecurity data and interpreted the relationship between two variables. The first variable, the dependent variable, is called the "risk level," which is classified into two categories: high and medium. The second variable, the independent variable, comprised three sub-variables. The first sub-variable, called the "type of cyberattack," included eight main types identified based on the most common cybersecurity threats: (phishing email attacks, malware insertion via attachment, Distributed Denial of Service (DDoS) attacks, SQL injection attacks, denial-of-service attacks on main servers, email or phone calls to steal accounts, and viruses spread via malicious links). The second sub-variable, called the "impact of the attack," included four types: (data loss, data disruption, service disruption, and data leakage). The third sub-variable, called the "security level," was divided into (low, medium, and high). These sub-variables were obtained by distributing a questionnaire to 175 cybersecurity professionals, which included questions about the perceived impact of breaches. The study examined cybersecurity database attacks and how to protect against them, analyzing them using a Bayesian classifier and the R statistical programming language. The results showed clear differences in the level of risk depending on the type of attack.
A Real-Time Vision-Based System for Driver Fatigue and Distraction Monitoring Using a Single In-Cabin RGB Camera
Driver fatigue and distraction are among the leading causes of road accidents worldwide, posing a critical challenge for intelligent transportation systems (ITS). Vision-based driver monitoring has emerged as a promising solution due to its non-intrusive nature and compatibility with in-vehicle environments. However, many existing approaches rely on isolated behavioural cues, lack real-time capability, or require specialized sensing hardware, which limits their practical deployment. This paper presents a real-time vision-based framework for monitoring driver fatigue and distraction using a single in-cabin RGB camera. The proposed framework integrates complementary behavioural indicators, including eye closure analysis based on PERCLOS, yawning detection, and head pose estimation, through a decision-level fusion strategy. The framework is evaluated on two widely adopted public benchmark datasets, namely the NTHU Drowsy Driver Detection dataset and the YAWDD dataset. Experimental results show that the proposed approach achieves detection accuracies of 92.6% on NTHU and 93.0% on YAWDD, with average processing latencies below 120 ms, satisfying real-time operational requirements. These results demonstrate that multi-cue visual analysis significantly improves detection robustness compared to single-cue methods while maintaining practical deployability. The proposed framework therefore provides an effective and scalable solution for real-time driver state monitoring and contributes to enhanced road safety in intelligent transportation systems.
A proposed method for computing convolution neural network algorithms
One kind of deep neural network that uses low-level input, such shapes and lines, to find more nuanced patterns is called a convolutional neural network (CNN). CNNs compare subsets of data using a kernel or filter through a variety of convolution processes. Training deep convolutional neural networks on large datasets requires days of GPU processing. Very low latency is necessary for self-driving automobiles to identify pedestrians. This study examines a suggested quick way to calculate the operations needed by convolutional neural network algorithms. In these situations, the speed at which convolution neural networks compute determines how well they perform. MATLAB is used to build this algorithm, and a single graphical user interface (GUI) window is used for all operations. When the approach is used instead of MATLAB's built-in functions, processing time is decreased. It saves 70% of the processing time when compared to the built-in features. This technique works on the widely recognized fast table method (FTM) principle.
A Self-Supervised Damage-Aware Vision Transformer for Image Enhancement in Low-Quality and Post-Conflict Environments
In such environment fraught with both conflict and scarcity of resources, images captured by surveillance cameras, mobile phones, digital cameras or unmanned aerial vehicles (UAV) tend to encounter multiple degradation types such as noise, blur, deficient illumination, and artifact in transmission. Typical image enhancement techniques and supervised deep learning models require large paired (low/high-quality) datasets. However, these are rarely obtainable under the restricted conditions of post-conflict environments. This paper proposes a new damage-aware, self-supervised image enhancement framework based on the architecture of Vision Transformer (ViT). The proposed model implicitly learns different forms of degradation and autonomously enhances image quality without the need for high-resolution reference images as ground-truth. A multi-task self-supervised objective that allows simultaneous recognition and restoration of degradation. Experimental results show that our technique improves perceptual quality, structural integrity, and robustness by a large margin, when contrasted with conventional CNN-based methods. Of significance is the fact that the algorithm is highly effective in its ability to handle unseen real-world and post-conflict degradation scenarios.