Explore the vast range of titles available.

Mobile devices face significant security challenges due to the increasing proliferation of Android malware. This study introduces an innovative approach to Android malware detection, combining Support Vector Regression (SVR) and dynamic feature analysis to address escalating mobile security challenges. Our research aimed to develop a more accurate and reliable malware detection system capable of identifying both known and novel malware variants. We implemented a comprehensive methodology encompassing dynamic feature extraction from Android applications, feature preprocessing and normalization, and the application of SVR with a Radial Basis Function (RBF) kernel for malware classification. Our results demonstrate the SVR-based model’s superior performance, achieving 95.74% accuracy, 94.76% precision, 98.06% recall, and a 96.38% F1-score, outperforming benchmark algorithms including SVM, Random Forest, and CNN. The model exhibited excellent discriminative ability with an Area Under the Curve (AUC) of 0.98 in ROC analysis. The proposed model’s capacity to capture complex, non-linear relationships in the feature space significantly enhanced its effectiveness in distinguishing between benign and malicious applications. This research provides a robust foundation for advancing Android malware detection systems, offering valuable insights for researchers and security practitioners in addressing evolving malware challenges.

Photodynamic cancer therapy circumvents the major side effects associated with the conventional cancer treatment methods, such as chemotherapy, surgery and exposure to radiation. Experimental measurement of photosensitizer quantum yield (PQY) singlet production of oxygen through either sensitive laser spectroscopy or luminescence detection at the wavelength of 1270 nm is costly; time consuming and intensive while unreliability of chemical traps experimental approach is of serious concern. Quantitative structure-activity relationship (QSAR) computational method proposed in the literature for computing PQY of singlet oxygen production has characteristics deviation from the measured values. PQY singlet oxygen production of twenty-nine pteridines photosensitizer compounds is modeled and predicted in this present contribution using extreme learning machine (ELM) and support vector regression (SVR) with hybridized particle swarm optimization (PSO) method for ensuring combinatory parameter selection. The performances of the developed SVR-PSO computational method are assessed using mean absolute error (MAE), correlation coefficient (CC), root mean square error (RMSE) and mean absolute percentage deviation (MAPD). The developed SVR-PSO model outperforms QSAR (2016) model with performance superiority of 34.78%, 3.65%, 17.64% and 42.16% on the basis of RMSE, CC, MAE and MAPD performance measuring parameters, respectively. The developed ELM-SINE (with sine activation function) and ELM-SIG (with sigmoid activation function) respectively outperform the existing QSAR (2016) model with improvement of 6.54% and 4.70% using R-squared metric. The demonstrated outstanding performance of the present predictive models is immensely meritorious in strengthening the potentials of alternative cancer therapy and circumventing the experimental challenges of PQY singlet oxygen production determination.

This study presents a comparative evaluation of Dijkstra’s algorithm, A*, Bellman-Ford, AI-Augmented A* and a neural AI-based model for shortest-path computation across diverse graph topologies, with a focus on time efficiency and memory consumption under standardized experimental conditions. We analyzed grids, random graphs, and scale-free graphs of sizes up to 103,103 nodes, specifically examining 100- and 1000-node grids, 100- and 1000-node random graphs, and 100-node scale-free graphs. The algorithms were benchmarked through repeated runs per condition on a server-class system equipped with an Intel Xeon Gold 6248R processor, NVIDIA Tesla V100 GPU (32 GB), 256 GB RAM, and Ubuntu 20.04. A* consistently outperformed Dijkstra’s algorithm when paired with an informative admissible heuristic, exhibiting faster runtimes by approximately 1.37× to 1.91× across various topologies. In comparison, Bellman-Ford was slower than Dijkstra’s by approximately 1.50× to 1.92×, depending on graph type and size; however, it remained a robust option in scenarios involving negative edge weights or when early-termination conditions reduced practical iterations. The AI model demonstrated the slowest performance across conditions, incurring runtimes that were 2.60× to 3.23× higher than A* and 1.62× to 2.15× higher than Bellman-Ford, offering limited gains as a direct solver. These findings underscore topology-sensitive trade-offs: A* is preferred when a suitable heuristic is available; Dijkstra’s serves as a strong baseline in the absence of heuristics; Bellman-Ford is appropriate for handling negative weights; and current AI approaches are not yet competitive for exact shortest paths but may hold promise as learned heuristics to augment A*. We provide environmental details and comparative results to support reproducibility and facilitate further investigation into hybrid learned-classical strategies.

Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP), focusing on identifying and extracting entities such as names, locations, organizations, and other specific labels from unstructured text data. It plays a crucial role in various NLP applications, including information retrieval, question answering, and sentiment analysis. However, while NER systems have been extensively developed for English, adapting them to languages like Urdu poses unique challenges due to linguistic differences and the scarcity of annotated data. In this research, we enhance data diversity and accessibility for Urdu NER by introducing the ZUNERA corpus , the most extensive Urdu NER dataset to date, comprising 1,189,614 tokens and 89,804 named entities. Additionally, we classify the entities into twenty-three different named entities types. We meticulously annotate the corpus , providing clear guidelines and employing the Kappa coefficient to ensure high-quality annotations. Furthermore, we propose the Urdu-Named Entity Recognition with BiGRU-based Deep Learning Architecture (NERD) framework, which facilitates efficient entity recognition in Urdu text. The proposed framework achieves an impressive F1-score of 94.6%. Comparing ZUNERA with the MK-PUCIT dataset underscores its robustness in accurately recognizing entities. Although this study centers on Urdu, the proposed NER framework and annotation pipeline are designed to be language-agnostic. They can be extended to other morphologically rich or low-resource languages, providing a replicable foundation for future cross-lingual research. Overall, our contributions significantly advance Urdu NER research by providing a comprehensive dataset, evaluating state-of-the-art techniques, and introducing a novel framework for efficient Urdu entity recognition.

Spinel ferrites are magnetic oxide materials with potentials to promote green technology in magnetic refrigeration which is known to be economically clean, energy saving and efficient. Maximum magnetic entropy change of spinel ferrites decides and controls the applicability as well as the strength of spinel ferrite magnetic oxide since it measures the hugeness of magnetocaloric effect. However, experimental determination of maximum magnetic entropy change requires intensive procedures, costly equipment and consumes appreciable time. Intelligent models are presented in this work using spinel-ferrite-molecular-based descriptors such as the ionic radii of spinel ferrites constituents, applied magnetic field and their concentrations. The developed intelligent models for prediction of spinel ferrite maximum magnetic entropy change include extreme learning machine (ELM) and hybrid genetic-algorithm-coupled support vector regression (GSVR). The developed ELM model has correlation coefficient (CC) and mean absolute error (MAE) of 98.45% and 0.117 J/kg/K, respectively, while the developed GSVR model has CC of 80.87% and MAE of 0.129 J/kg/J. The developed ELM model which is based on empirical risk minimization principle shows better performance over GSVR model that premises on structural minimization risk principle with improvement of 0.06%, 17.86% and 8.765% using root mean square error, CC and MAE yardsticks, respectively. Closeness of the estimates of the developed models with the experimental values is a strong indication of the potentials of the proposed intelligent methods in facilitating practical implementation of magnetic cooling refrigeration to solve energy crisis which promote efficiency and environmental friendliness.

Zinc selenide (ZnSe) nanomaterial is a binary semiconducting material with unique features, such as high chemical stability, high photosensitivity, low cost, great excitation binding energy, non-toxicity, and a tunable direct wide band gap. These characteristics contribute significantly to its wide usage as sensors, optical filters, photo-catalysts, optical recording materials, and photovoltaics, among others. The light energy harvesting capacity of this material can be enhanced and tailored to meet the required application demand through band gap tuning with compositional modulation, which influences the nano-structural size, as well as the crystal distortion of the semiconductor. This present work provides novel ways whereby the wide energy band gap of zinc selenide can be effectively modulated and tuned for light energy harvesting capacity enhancement by hybridizing a support vector regression algorithm (SVR) with a genetic algorithm (GA) for parameter combinatory optimization. The effectiveness of the SVR-GA model is compared with the stepwise regression (SPR)-based model using several performance evaluation metrics. The developed SVR-GA model outperforms the SPR model using the root mean square error metric, with a performance improvement of 33.68%, while a similar performance superiority is demonstrated by the SVR-GA model over the SPR using other performance metrics. The intelligent zinc selenide energy band gap modulation proposed in this work will facilitate the fabrication of zinc selenide-based sensors with enhanced light energy harvesting capacity at a reduced cost, with the circumvention of experimental stress.

Purpose: The first novel coronavirus disease-19 (COVID-19) case in the Kingdom of Saudi Arabia (KSA) was reported in Qatif in March 2020 with continual increase in infection and mortality rates since then. In this study, we aim to determine risk factors which effect severity and mortality rates in a cohort of hospitalized COVID-19 patients in KSA. Method: We reviewed medical records of hospitalized patients with confirmed COVID-19 positive results via reverse-transcriptase-polymerase-chain-reaction (RT-PCR) tests at Prince Mohammed Bin Abdulaziz Hospital, Riyadh between May and August 2020. Data were obtained for patient's demography, body mass index (BMI), and comorbidities. Additional data on patients that required intensive care unit (ICU) admission and clinical outcomes were recorded and analyzed with Python Pandas. Results: A total of 565 COVID-19 positive patients were inducted in the study out of which, 63 (11.1%) patients died while 101 (17.9%) patients required ICU admission. Disease incidences were significantly higher in males and non-Saudi nationals. Patients with cardiovascular, respiratory, and renal diseases displayed significantly higher association with ICU admissions (p<0.001) while mortality rates were significantly higher in COVID-19 patients with cardiovascular, respiratory, renal and neurological diseases. Univariate cox proportional hazards regression model showed that COVID-19 positive patients requiring ICU admission [Hazard's ratio, HR=4.2 95% confidence interval, CI 2.5-7.2); p<0.001] with preexisting cardiovascular [HR=4.1 (CI 2.5-6.7); p<0.001] or respiratory [HR=4.0 (CI 2.0- 8.1); p=0.010] diseases were at significantly higher risk for mortality among the positive patients. There were no significant differences in mortality rates or ICU admissions among males and females, and across different age groups, BMIs and nationalities. Hospitalized patients with cardiovascular comorbidity had the highest risk of death (HR=2.9, CI 1.7-5.0; p=0.020). Conclusion: Independent risk factors for critical outcomes among COVID-19 in KSA include cardiovascular, respiratory and renal comorbidities. Keywords: COVID-19, SARS-CoV-2, mortality rate, comorbidities, risk factors, KSA

Cuprous oxide (Cu 2 O) is a p-type metal oxide semiconducting material with potential in photovoltaic and photocatalysis applications due to its excellent absorption capacity in visible region and tunable energy gap. Experimental synthesis and energy gap characterization of thin film cuprous oxide semiconductor with desired dopants and varying experimental conditions for enhanced photocatalytic as well as photovoltaic activities are laborious and consume appreciable precious resources. This work hybridizes particle swarm optimization method with support vector regression algorithm for computing energy gap of thin film cuprous oxide semiconductor using the thickness of thin film and distorted lattice parameter as descriptors. The predictions of swarm-based support vector regression (S-SVR) model are compared with estimates of stepwise regression (SR) model while S-SVR shows superior performance of 39.47 %, 36.20 % and 114.41 % on testing data samples over SR model using root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC), respectively. The developed S-SVR model is characterized with 0.9559 CC, 0.0586 MAE and 0.028 RMSE on the basis of training samples. The developed S-SVR and SR models were further validated using external data samples while the developed S-SVR demonstrates excellent agreement with the measured values. The convincing precision demonstrated by S-SVR model would be of indispensable significance in determining energy gap of cuprous oxide semiconductor (for photocatalytic applications in pollutant removal, solar cell, gas sensors and thin film transistors) with appreciable quickness and reduced cost coupled with experimental difficulty circumvention.

Accreditation is widely considered to be a vital tool for quality assurance in higher education; however, there is disagreement in the academic community on the intended benefits of accreditation. Preparing for accreditation requires extensive financial and human resources to complete the required documentation. All accreditation agencies require improvements in institutional infrastructure, enhanced student support, appropriate learning environments, and faculty development, which can directly improve students’ learning experiences. In this paper, we explore the impact of accreditation on students’ learning by using a case study-based approach. We selected four degree programs from a University in Saudi Arabia and compared the performances of students in different courses before and after acquiring local program accreditation (NCAAA). The results highlight that although there is no direct relationship between increased student performance and acquiring accreditation, there is a significant impact on the performance of student learning. However, there is a need for sustained efforts to continuously adopt accreditation-aligned practices to gain a sustained advantage. We have presented a model that can enable academic institutions to continuously adhere to best practices even if no accreditation visit has been scheduled in the near future. This way, academic programs can consistently improve their processes and enhance student learning.