Explore the vast range of titles available.

After the digital revolution, large quantities of data have been generated with time through various networks. The networks have made the process of data analysis very difficult by detecting attacks using suitable techniques. While Intrusion Detection Systems (IDSs) secure resources against threats, they still face challenges in improving detection accuracy, reducing false alarm rates, and detecting the unknown ones. This paper presents a framework to integrate data mining classification algorithms and association rules to implement network intrusion detection. Several experiments have been performed and evaluated to assess various machine learning classifiers based on the KDD99 intrusion dataset. Our study focuses on several data mining algorithms such as; naïve Bayes, decision trees, support vector machines, decision tables, k-nearest neighbor algorithms, and artificial neural networks. Moreover, this paper is concerned with the association process in creating attack rules to identify those in the network audit data, by utilizing a KDD99 dataset anomaly detection. The focus is on false negative and false positive performance metrics to enhance the detection rate of the intrusion detection system. The implemented experiments compare the results of each algorithm and demonstrate that the decision tree is the most powerful algorithm as it has the highest accuracy (0.992) and the lowest false positive rate (0.009).

The rapid growth of the use of social media opens up new challenges and opportunities to analyze various aspects and patterns in communication. In-text mining, several techniques are available such as information clustering, extraction, summarization, classification. In this study, a text mining framework was presented which consists of 4 phases retrieving, processing, indexing, and mine association rule phase. It is applied by using the association rule mining technique to check the associated term with the Huawei P30 Pro phone. Customer reviews are extracted from many websites and Facebook groups, such as re-view.cnet.com, CNET. Facebook and amazon.com technology, where customers from all over the world placed their notes on cell phones. In this analysis, a total of 192 reviews of Huawei P30 Pro were collected to evaluate them by text mining techniques. The findings demonstrate that Huawei P30 Pro, has strong points such as the best safety, high-quality camera, battery that lasts more than 24 hours, and the processor is very fast. This paper aims to prove that text mining decreases human efforts by recognizing significant documents. This will lead to improving the awareness of customers to choose their products and at the same time sales managers also get to know what their products were accepted by customers suspended.

Data compression is one of the core fields of study for applications of image and video processing. The raw data to be transmitted consumes large bandwidth and requires huge storage space as a result, it is desirable to represent the information in the data with considerably fewer bits by the mean of data compression techniques, the data must be reconstituted very similarly to the initial form. In this paper, a hybrid compression based on Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) is used to enhance the quality of the reconstructed image. These techniques are followed by entropy encoding such as Huffman coding to give additional compression. Huffman coding is optimal prefix code because of its implementation is more simple, faster, and easier than other codes. It needs less execution time and it is the shortest average length and the measurements for analysis are based upon Compression Ratio, Mean Square Error (MSE), and Peak Signal to Noise Ratio (PSNR). We applied a hybrid algorithm on (DWT–DCT 2 × 2, 4 × 4, 8 × 8, 16 × 16, 32 × 32) blocks. Finally, we show that by using a hybrid (DWT–DCT) compression technique, the PSNR is reconstructed for the image by using the proposed hybrid algorithm (DWT–DCT 8 × 8 block) is quite high than DCT.

The majority decisions of online customers make are by tracing the electronic word of mouth and online comments which belong to previous customers and is affected by some fears. This study applied a decision tree method to customer data of those who visit a popular group on Facebook (SouqEgypt). Findings in this study indicated that social media marketing for increasing customer's retention and loyalty are influenced by customer's income, education level and occupation. This study helps marketing managers to enhance customer loyalty and in the long run maximize returns on marketing.

Background High rates of non-prescription dispensing of antimicrobials have led to a significant increase in the antimicrobial overuse and misuse in Saudi Arabia (SA). The objective of this study was to evaluate the antimicrobial utilization following the enforcement of a new prescription-only antimicrobial dispensing policy in the community pharmacy setting in SA. Methods Data were extracted from the IQVIA database between May 2017 and May 2019. The antimicrobial utilization rates, based on sales, defined daily dose in grams (DDD), DDD/1000 inhabitants/day (DID), and antimicrobial-claims for the pre-policy (May 2017 to April 2018) and post-policy (June 2018 to May 2019) periods were assessed. Results Overall antimicrobial utilization declined slightly (~9-10%) in the post-policy versus pre-policy period (sales, 31,334 versus 34,492 thousand units; DDD, 183,134 versus 202,936), with higher claims (~16%) after policy implementation. There was a sudden drop in the utilization rate immediately after policy enforcement; however, the values increased subsequently, closely matching the pre-policy values. Utilization patterns were similar in both periods; penicillin was the most used antimicrobial (sales: 11,648-14,700-thousand units; DDD: 71,038-91,227; DID: 2.88-3.78). For both periods, the highest dip in utilization was observed in July (sales: 1,027-1,559 thousand units; DDD: 6,194-9,399), while the highest spike was in March/October (sales: 3,346-3,884 thousand units; DDD: 22,329-19,453). Conclusion Non-prescription antimicrobial utilization reduced minimally following policy implementation in the community pharmacies across SA. Effective implementation of prescription-only regulations is necessary.

The objective of the present work is for assessing ergonomics-based IoT (Internet of Things) related healthcare issues with the use of a popular multi-criteria decision-making technique named the analytic hierarchy process (AHP). Multiple criteria decision making (MCDM) is a technique that combines alternative performance across numerous contradicting, qualitative, and/or quantitative criteria, resulting in a solution requiring a consensus. The AHP is a flexible strategy for organizing and simplifying complex MCDM concerns by disassembling a compound decision problem into an ordered array of relational decision components (evaluation criteria, sub-criteria, and substitutions). A total of twelve IoT-related ergonomics-based healthcare issues have been recognized as Lumbago (lower backache), Cervicalgia (neck ache), shoulder pain; digital eye strain, hearing impairment, carpal tunnel syndrome; distress, exhaustion, depression; obesity, high blood pressure, hyperglycemia. “Distress” has proven itself the most critical IoT-related ergonomics-based healthcare issue, followed by obesity, depression, and exhaustion. These IoT-related ergonomics-based healthcare issues in four categories (excruciating issues, eye-ear-nerve issues, psychosocial issues, and persistent issues) have been compared and ranked. Based on calculated mathematical values, “psychosocial issues” have been ranked in the first position followed by “persistent issues” and “eye-ear-nerve issues”. In several industrial systems, the results may be of vital importance for increasing the efficiency of human force, particularly a human–computer interface for prolonged hours.

Pneumonia is a significant cause of morbidity and mortality among adults globally. This retrospective cohort analysis assessed the pneumonia burden and related healthcare resource utilization and costs in the at-risk (low, medium, and high-risk) adult patients in Dubai, United Arab Emirates (UAE). The claims data from January 1, 2014 to June 30, 2019 were extracted from the Dubai Real-World Claims Database for patients, aged [greater than or equal to]18 year, having at least 1 pneumonia claim. Data for the inpatient, outpatient and emergency visits were assessed for 12-months, before (pre-index) and after (follow-up) a pneumonia episode. Healthcare costs were calculated based on dollar value of 2020. Total 48,562 records of eligible patients were analyzed (mean age = 39.9 years; low [62.1%], medium [36.2%] and high [1.7%] risk cohorts). Mean all-cause healthcare costs were approximately >45% higher in the follow-up period (1,947 USD/patient) versus pre-index period (1,327 USD/patient). During follow-up period, the mean annual pneumonia incidence rate was 1.3 episodes, with a similar pattern across all cohorts. Overall, mean claims and costs (USD) per patient (all-cause) were highest in the high-risk cohort in the follow-up period (claims: overall, 11.6; high-risk, 22.0; medium-risk, 13.9; low-risk, 9.9; costs: high-risk, 14,184; medium-risk, 2,240; low-risk, 1,388). Similarly, the mean pneumonia-related costs (USD) per patient were highest for the high-risk cohort (overall: 1,305; high-risk, 10,207; medium-risk, 1,283; low-risk, 882), however, the claims were similar across cohorts (claims/patient: overall: 2.0; high-risk, 1.9; medium-risk, 2.2; low-risk, 1.9). Most all-cause and pneumonia-related costs were due to inpatient visits (4,901 and 4,818 USD respectively), while outpatient (1,232 and 166 USD respectively) and emergency visits (347 and 206 USD respectively) contributed significantly lesser. Pneumonia imposes a significant healthcare burden in the UAE, especially in the high-risk patients with severe comorbidities. These findings would guide clinicians and policy makers to make informed decisions.

MRI showing an aggressive infiltrating lesion invading the pelvis and lumber region that extends from the posterior abdominal wall, with a sizable intrapelvic and intraabdominal extension infiltrating the lumber and sacral vertebrae and an intraspinal epidural extension displacing the abdominal organ to the left; in addition, there were many \"dot-in sign\" of mycetoma. http://dx.doi.org/10.1371/journal.pntd.0004945.g002 In June 2015, he was admitted with severe watery diarrhea, dyspnea, and a productive cough containing black grains for a hospital stay of one month (Fig 3). Key Learning Points * Mycetoma is a localized disease but rarely can spread by the lymphatics and blood. * Mycetoma can have a progressive, aggressive, and wild clinical course. * Mycetoma can spread to distant organs such as the lung and spinal cord. * Some patients may not respond to the available medical and surgical treatment, and this can result in fatality. * The depressed immune system in the patient described here may be the cause of the aggressive disease and pulmonary spread.

Background The purpose of this study was to assess the impact of survival improvements and performance-based managed entry agreements (PBMEAs) on the cost implications of introducing innovative spinal muscular atrophy (SMA) treatments, nusinersen, onasemnogene abeparvovec, and risdiplam, for managing SMA Types 1, 2, and 3 from the perspective of the Saudi Ministry of Health (MoH). Methods A budget impact model was created using inputs such as total population, market share, median survival, and resource utilization obtained through literature review and validated by expert committees. The model projected the overall cost (drug acquisition, administration, and disease management) for best supportive care (BSC) with and without these interventions over a 5-year period using Microsoft Excel as the analytical tool. Results For SMA Type 1, the overall net budget impact of introducing onasemnogene abeparvovec, nusinersen, or risdiplam was significant, ranging from 112 to 225%. The impact was even greater for SMA Type 2 and 3, ranging from 171 to 283% due to high survival rates. However, the budget impact could be mitigated by improved clinical management and PBMEAs, reducing it to 77–84% for Type 1 and 36–117% for Types 2 and 3. Conclusion the introduction of these pioneering interventions for SMA management would raise the overall budget for the payer, primarily due to drug acquisition costs. Nevertheless, this increase could be offset by improvements in clinical management and PBMEAs.

A brain tumor can have an impact on the symmetry of a person’s face or head, depending on its location and size. If a brain tumor is located in an area that affects the muscles responsible for facial symmetry, it can cause asymmetry. However, not all brain tumors cause asymmetry. Some tumors may be located in areas that do not affect facial symmetry or head shape. Additionally, the asymmetry caused by a brain tumor may be subtle and not easily noticeable, especially in the early stages of the condition. Brain tumor classification using deep learning involves using artificial neural networks to analyze medical images of the brain and classify them as either benign (not cancerous) or malignant (cancerous). In the field of medical imaging, Convolutional Neural Networks (CNN) have been used for tasks such as the classification of brain tumors. These models can then be used to assist in the diagnosis of brain tumors in new cases. Brain tissues can be analyzed using magnetic resonance imaging (MRI). By misdiagnosing forms of brain tumors, patients’ chances of survival will be significantly lowered. Checking the patient’s MRI scans is a common way to detect existing brain tumors. This approach takes a long time and is prone to human mistakes when dealing with large amounts of data and various kinds of brain tumors. In our proposed research, Convolutional Neural Network (CNN) models were trained to detect the three most prevalent forms of brain tumors, i.e., Glioma, Meningioma, and Pituitary; they were optimized using Aquila Optimizer (AQO), which was used for the initial population generation and modification for the selected dataset, dividing it into 80% for the training set and 20% for the testing set. We used the VGG-16, VGG-19, and Inception-V3 architectures with AQO optimizer for the training and validation of the brain tumor dataset and to obtain the best accuracy of 98.95% for the VGG-19 model.