المجلة الدولية للحاسبات والمعلوماتية IJCI هي مجلة علمية محكمة دولية متخصصة في مجالات علوم الحاسب وهندسة البرمجيات ونظم المعلومات وتكنولوجيا المعلومات وعلوم البيانات ونظم المعلومات الإدارية، تصدرها دار النشر “رؤية للبحوث العلمية والنشر” Vision for Scientific Research and Publishing الكائنة في لندن بالمملكة المتحدة، والتي تدار من قبل أكاديميين ذو خبرات علمية طويلة. وتهتم المجلة بنشر الأبحاث العلمية التي يتوافر فيها الأصالة والحداثة والمنهجية العلمية والتي تشكل إضافة علمية مفيدة سواء باللغة العربية أو الإنجليزية. وتخضع البحوث المنشورة في المجلة لعملية تحكيم على يد نخبة من الأساتذة الأكاديميين المتخصصين من العديد من دول العالم. كما تتولى المجلة القيام بفهرسة وأرشفة الإنتاج العلمي والمعرفي العربي في كبرى قواعد البيانات العلمية العالمية.
Vision for Scientific Research and Publishing
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.
الحقوق الفكرية (c) 2026 المجلة الدولية للحاسبات والمعلوماتية
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.
جدوى تطبيق نظام إدارة تذاكر القطارات في العراق بناءً على الخبرة العالمية
تبحث هذه الدراسة إمكانية تبني نظام رقمي حديث لإدارة تذاكر القطارات في العراق، والاستفادة من الخبرات والممارسات المتبعة في دول العالمية المتقدمة في هذا المجال مثل الولايات المتحدة وروسيا وتركيا. وتعتمد الدراسة على منظور تحليلي مقارن مدعوم بتصميم نظام مفاهيمي من خلال التحليل الذي يستند الى المقارنة تدرس الفجوة بين الوضع الحالي لخدمات ادارة القطارات في العراق والأنظمة الدولية الأكثر تطوراً. وتشير النتائج إلى وجود عدة نقاط ضعف، لا سيما في البنية التحتية للتذاكر الرقمية، ومعالجة البيانات، ومستوى تكامل الرقمي للنظام. ولمعالجة هذه المشكلات، تقترح الدراسة خطة تنفيذ تدريجية تبدأ بخدمات رقمية بسيطة وتتطور بمرور الوقت إلى نظام نقل ذكي أكثر تكاملاً. كما تشير النتائج إلى أن الاستفادة من الممارسات الدولية الناجحة وخاصة في مجالات مثل التذاكر الإلكترونية، وطرق الدفع الرقمية، واستخدام البيانات في العمليات يمكن أن يحسن الكفاءة من خلال رضى المسافرين. وتُساهم هذه التحسينات في تبسيط الإجراءات، وتسهيل الوصول إلى الخدمات، ودعم التكامل المستقبلي مع حلول النقل الذكية الأوسع نطاقًا. وتوصلت الدراسة الى نتائج اهمها أن العراق يمتلك فرصة حقيقية لتطوير نظام لحجز تذاكر إدارة القطارات حديث وفعّال بالاستفادة من الخبرة الأجنبية التي حققت رضى المسافرين بالإضافة الى المكاسب الاقتصادية الكبيرة، شريطة أن يدعم هذا التطوير بنية تحتية بطرق صحيحة وفق معايير واسس دولية، واعتماد تدريجي للتكنولوجيا بما يتماشى مع المعايير الدولية.
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.
التفكير التصميمي: مفهومه ومراحله وأدواته ودوره في تصميم تجربة التعلم
يتناول هذا البحث الذي يأتي بعنوان: (التفكير التصميمي: مفهومه ومراحله وأدواته ودوره في تصميم تجربة التعلم) منهجية التفكير التصميمي (Design Thinking) كإطار عمل ابتكاري لمواجهة التحديات التعليمية المعقدة وتطوير بيئات التعلم بما يتماشى مع تطلعات رؤية المملكة 2030. كما هدف إلى استكشاف مفهوم التفكير التصميمي، وتحديد مراحله الخمس الأساسية (التعاطف، تعريف المشكلة، توليد الأفكار، النمذجة الأولية، والاختبار)، بالإضافة إلى إبراز دوره في تصميم تجربة التعلم (LXD) واستخدمت فيه الباحثات المنهج الوصفي التحليلي من خلال مراجعة الأدبيات والأطر النظرية والأدوات التقنية والورقية التي تدعم منهجية التفكير التصميمي. وتوصلت الباحثات إلى مجموعة من النتائج أبرزها: أن التفكير التصميمي يمثل تحولاً جذرياً نحو التعليم المتمركز حول المتعلم من خلال جعل "التعاطف" نقطة الانطلاق لفهم الاحتياجات الحقيقية للمتعلمين. وأثبتت النتائج فاعلية هذه المنهجية في تنمية مهارات القرن الحادي والعشرين، مثل التفكير الإبداعي، والتعاون، والقدرة على حل المشكلات غير الواضحة. وخلص البحث إلى أن التكامل بين التفكير التصميمي وتصميم تجربة التعلم يساهم في بناء نماذج تعليمية مرنة وتكرارية تزيد من تفاعل المتعلمين وتحسن نواتج التعلم. وأوصت الدراسة بضرورة تدريب الكوادر التعليمية على أدوات التفكير التصميمي وتوفير بيئة مؤسسية تدعم ثقافة الابتكار والتجريب.
مشاكل الحوكمة الإلكترونية التقنية وطرق نجاح التحول الرقمي في القطاع الحكومي
تسعى الدراسة الحالية إلى التعرف على تأثير أبعاد الحوكمة الرقمية (البنية التحتية للتكنولوجيا والعمليات التشغيلية، الإدارة والسلطات، المساءلة والشفافية، إدارة المخاطر الرقمية) مجتمعة على التحول الرقمي أن عصر التحول الرقمي يمثل "تطبيق القدرات الرقمية على العمليات والمنتجات والأصول" بهدف "تحسين الكفاءة، وتعزيز القيمة لدى العملاء، وإدارة المخاطر، وكشف فرص جديدة لتحقيق الدخل" وبالمثل أن التحول الرقمي لا يؤثر في الحياة فحسب، بل يؤثر أيضًا على تجارب الأفراد. كما تركز هذه الدراسة على الآليات اللازمة لتحويل التفاعل بين الحكومات والمواطنين رقميًا لضمان مزيد من الشفافية والمساءلة والنزاهة. وتسهم هذه الدراسة في تحديد واقع الحوكمة الإلكترونية والنضج الرقمي في المؤسسات الحكومية.
تناولت هذه الدراسة استشراف مستقبل الابتكار في التعليم الإلكتروني بوصفه رؤية علمية تهدف لتلافي الأخطاء السابقة وبناء نماذج تعليمية مبتكرة. استعرضت الدراسة التحولات العالمية التي فرضتها جائحة كوفيد-19، والتي أدت إلى تسارع التحول الرقمي وتبني تقنيات متقدمة مثل أنظمة إدارة التعلم (LMS) والتعلم التكيفي. كما ركزت الدراسة على الاتجاهات الحديثة المتمثلة في توظيف الذكاء الاصطناعي، والواقع الافتراضي، والتعلم عبر الأجهزة المحمولة لتخصيص تجربة المتعلم وتحسين جودة المخرجات. وخلصت النتائج إلى أهمية الشهادات المصغرة (Micro-credentials) والمسارات المرنة في دعم مفهوم التعلم مدى الحياة ومواءمة التعليم مع متطلبات سوق العمل. ورغم الفرص التقنية الواسعة، حددت الدراسة مجموعة من التحديات الجوهرية، أبرزها ضمان العدالة والإنصاف الرقمي، وحماية الخصوصية، وتطوير المناهج لتواكب المتغيرات المتسارعة. وأوصت الدراسة بضرورة دعم المبادرات الابتكارية والمؤسسية لضمان استدامة التعليم الرقمي في المستقبل.
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.
نموذج بيزي لتقدير مخاطر الأمن السيبراني بالاعتماد على مصنف Naive Bayes
تناول البحث تحليل بيانات الأمن السيبراني وتفسير العلاقة بين متغيرين الأول يمثل المتغير المعتمد الذي يطلق عليه (مستوى الخطر) والذي يصنف إلى قسمين (عالي ومتوسط) والمتغير المستقل والذي تضمن ثلاثة أنواع من المتغيرات وهي المتغير الأول ويطلق عليه (نوع الهجوم السيبراني) ويشمل ثمانية أنواع رئيسية تم تحديدها استنادًا إلى أكثر التهديدات شيوعًا في مجال الأمن السيبراني، وهي: (هجوم عبر بريد إلكتروني احتيالي، إدخال برامج ضارة عبر مرفق، هجمات حجب الخدمة Distributed Denial of Service (DDOS)، هجوم عبر حقن Structured Query Language (SQL) في الموقع، هجوم حجب الخدمة على الخوادم الرئيسية، رسائل بريد إلكتروني أو اتصال هاتفي لسرقة الحسابات، فيروس ينُشر عبر رابط ضار)، والمتغير الثاني يطلق عليه (تأثير الهجوم) والذي تضمن أربعة أنواع (فقدان البيانات، تعطل البيانات، تعطل الخدمة، تسريب البيانات) والمتغير الثالث يدعى (مستوى الأمن) ويقسم إلى (منخفض، متوسط، عالي) وتم الحصول عليها من خلال تصميم استمارة معلومات وزعت على (175) من متخصصين بالأمن السيبراني وتضمنت أسئلة حول مدى التأثير المتحقق من الاختراقات التي تجتاح قاعدة بيانات الأمن السيبراني وكيفية الحماية منها، وتحليلها باستخدام مصنف بيز من خلال (لغة البرمجة الإحصائية R)، وأظهرت النتائج وجود فروق واضحة في مستوى الخطورة تبعًا لنوع الهجوم.
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.
A Comprehensive Model for Evaluating Website Competence Using Web Mining Techniques
Evaluating websites has become increasingly critical as they function as primary platforms for information dissemination, communication, and digital service delivery. Website quality directly affects user experience and significantly influences the perceived credibility of online content. Although numerous evaluation models have been proposed, many remain constrained by manual assessment procedures, limiting their scalability and suitability for dynamic web environments. To address these limitations, this study proposes a web-mining–driven evaluation model for assessing website competence through automated data extraction and weighted criteria analysis. The model integrates quantifiable indicators encompassing usability, content relevance, accessibility, reliability, and overall performance across multiple domains. Model robustness was validated through expert review and user-based evaluation. Statistical analysis demonstrates strong internal consistency (Cronbach’s alpha = 0.88), while content validity of the survey instrument—confirmed through expert evaluation—reached 0.94. Experimental results further indicate that the proposed approach outperforms existing methods, achieving 95% classification accuracy, high scalability, and an 80% user satisfaction rate, particularly in real-time evaluation contexts. Overall, the proposed framework offers a transparent, evidence-based mechanism for website quality assessment. By minimizing subjectivity and enabling automated analysis, it enhances digital trust and supports informed decision-making for developers, policymakers, and end users across diverse application domains.
Data-Driven Analysis of the Psychosocial and Behavioral Effects of Social Media Usage in Arabian Gulf Societies
The swift digitization of Gulf societies has changed the ways people communicate and present themselves, but has not yet sufficiently measured its psychosocial impact. This paper introduces a data-driven analysis of behavioral and emotional effects of social-media use in six Arabian Gulf nations: Saudi Arabia, United Arab Emirates, Kuwait, Qatar, Bahrain, and Oman. The analysis incorporates descriptive statistics, correlation matrices, inferential testing, and regression modeling to utilize a structured data set of twenty demographic, behavioral, and psychosocial variables to assess how online activity informs psychological health. Findings indicate that daily hours of use and intensity are positively associated with anxiety and sleep disturbance, while the frequency of peer comparisons and experiences of cyberbullying are associated with anxiety and low self-confidence. Gender and age differences also reveal young females as being more vulnerable to pressures based on appearance and stresses related to social validation. The findings provide a theoretical basis of understanding intersecting behavioral activity, cultural context, and psychosocial ramifications in digitally immersed Gulf communities, as well as underscore the importance of contextualized digital-wellness programming, social-media-literacy education, and policy responses aiming to reduce anxiety related to comparisons and foster good online behavior.
الحقوق الفكرية (c) 2025 المجلة الدولية للحاسبات والمعلوماتية
IoT Security and Privacy Issues: A Taxonomy
The Internet of Things, IoT, is affecting almost every part of our life and it is connecting us in unprecedented ways. Every device we carry, own, or have at home or in office could be connected to the internet. It could be our air-conditioning, alarm system, smoke detector, doorbell, refrigerator, TV, water/energy meters, parking meters, pollution detectors, car navigation systems, and public or private transportation vehicles. This will lead us to be more in touch with our surroundings and eventually turn our communities and cities into fully integrated, smart, sustainable intelligent entities. All IoT-based transformations are now occurring to increase productivity and create a coordinated world across business and technologies, and to make our lives smarter and easier, however, this, as with any new technology, often comes with a cost in terms of new security and privacy challenges and risks. We, as users of this new technology are often too preoccupied by its wonderful benefits that we don’t even think about any possible privacy risks or security issues that this new technology might introduce. Moreover, the exchange, storage, processing and transfer of tremendous amounts of sensitive information has also given rise to severe security and privacy concerns that compromise the efficiency and usability of IoT. It has become a challenge for users to depend on such a vulnerable technology, where the IoT security risks outweigh its benefits. Hence, security and privacy requirement is one of the key challenges to the IoT’s growth and success, and different IoT applications and devices may require different levels of security. In this paper we review and classify the different security and privacy issues pertaining to IoT and their different uses and applications, and we discuss the different measures that can be put in place to alleviate and address these issues.