120 research outputs found
Ranked Features Selection with MSBRG Algorithm and Rules Classifiers for Cervical Cancer
Get PDFIn this paper, an automatic three-phase cervical cancer diagnosis system is employed which includes feature extraction, feature selection followed by classification. Firstly, the modified seed-based region growing (MSBRG) algorithm is implemented for automatic segmentation and feature extraction using 500 cervical cancer cells. Processes to obtain the threshold values and the initial seed location are carried out automatically using moving k-mean (MKM) algorithm and invariant moment techniques. Secondly, eight attribute evaluators are applied for selecting and ranking the features, which are Correlation-based Feature Selection, Classifier Attribute Evaluator, Correlation Attribute Evaluator, Gain Ratio, Info Gain, OneR, ReliefF, and Symmetrical Uncertainty. Finally, the classification is compared based on five classifiers: Decision Table, JRip, OneR, PART, and ZeroR. The performance of the classifiers is evaluated using 3 test options: the training percentage splits (50% to 98%), the full training data and the cross validation (2-fold to 10-fold). The experimental results prove the capability of the MSBRG algorithm as an automatic feature extraction method. Furthermore, this paper proves the ability of the ranked feature selection methods to select important features of a cervical cell, and favors the Decision Table as the best classifier for cervical cancer classification
An Intelligent Classification System For Aggregate Based On Image Processing And Neural Network
Get PDFBentuk dan tekstur permukaan aggregat mempengaruhi kekuatan dan struktur konkrit. Secara tradisi, mesin pengayakan mekanikal dan pengukuran manual digunakan bagi menentukan kedua-dua saiz dan bentuk aggregat.
Aggregate’s shape and surface texture immensely influence the strength and structure of the resulting concrete. Traditionally, mechanical sieving and manual gauging are used
to determine both the size and shape of the aggregates
Al-Ri'ayah Al-Sihhiyah Wa Al-Ijtima'iyah Li Marda Al-Idz f'i Al-Shari'ah Al-Islamiyah
No full textThis paper was submitted to the symposium on AIDS and its social problems which was held by the Islamic Organization for Medical Sciences (IOMS) in Kuwait during the period 6-8 December 1993. The author sketches the broad lines of health and social care, from an Islamic perspective, to be provided for people who have contracted AIDS. The article handles different social issues such employment and marriage of AIDS patients. At the end of the article, the author discusses briefly the question whether AIDS falls within the category of death-sickness (marad al-mawt)as defined in Islamic jurisprudence
A Novel Approach for Enhancing Plant Leaf Classification With the Binary Cuckoo Search Algorithm
No full textComputer vision plays a crucial role in current day tasks due to its wide range of merits and applications in many fields like disease diagnosis, medical decisions, military, security, scientific applications, and business. Identifying plant species upon their leaf images is a very challenging and complex task. In this study, we introduced a model for classifying a variety set of plant leaves using different techniques such as factorization machine (FM), dimensionality reduction (DR), and ensemble learning (EL). In our study, FM is used as a classification algorithm, whereas DR is included in two stages, namely, feature extraction (FE) and feature selection (FS). FE has been used to extract the features from images in tabular form; on the other hand, FS is used to reduce the size of the data by getting rid of noisy (redundant and irrelevant) features. In addition, we utilized two methods for FS, filter-based and wrapper-based approaches. The used filter was the minimum redundancy maximum relevance, and the used wrapper was the improved binary cuckoo search algorithm that is based on Lévy flight and abandon nest functions. Regarding EL, it was used to declare different versions of FM by passing different subsets of features that are deduced from the original ones to improve its performance. We used the Swedish and Flavia leaf datasets for training and testing phases. The proposed model achieved a high performance in terms of accuracy that produced 95.67% on Swedish dataset and 99.6% on Flavia dataset. According to the results and in comparison to other methods, we proved that our proposed model ensures a favorable plant leaf classification approach. Finally, the proposed wrapper FS approach showed very good results without setting up the number of features to be selected by the user. The entire implementation of this work can be found at https://github.com/Mohammed-Ryiad-Eiadeh/Binary-Cuckoo-Search-For-Plant-Leaf-Prediction
3-way Interaction Testing Using the Tree Strategy
No full textAbstractFailures of hardware and software systems are often caused due to unexpected interactions among system components. The number of tests that needs to be performed in order to test all possible combinations of interactions can be exorbitant even for medium sized projects. To bring a balance between exhaustive testing and lack of testing, researchers have adopted pairwise testing which promises the testing of all pairwise combinatorial interactions between input components. This paper enhances the previous strategy, “A Tree Based Strategy for Test Data Generation and Cost Calculation” to go beyond pairwise testing. The new strategy can support 3-way combinatorial interaction testing
العوامل المرتبطة بالعدوى الطفيلية المعوية بين المرضى الذين حضروا الى عيادات الرعاية الصحية الأولية في أريحا
No full textBackground: Intestinal parasitic infections are a global issue, affecting millions of people, especially those in impoverished regions, with helminth diseases, particularly among schoolchildren. The Palestinian Ministry of Health (PMOH) has annually reported several cases of intestinal parasitic infestations, including giardiasis, ascariasis, and enterobiasis, strongyloidiasis, and amebiasis. The most significant risk factors for intestinal helminthic infections are low socioeconomic status, limited awareness of personal hygiene, and environmental contamination. Due to parasitic infection, anemia is considered multifactorial, which is associated primarily with iron deficiency and undernourishment in children.
Aim: The aim of the study is to assess the risk factors associated with Intestinal Parasitic Infection among patients attending Jericho Governmental Primary Health Care Clinics.
Study Methodology: A cross-sectional study was conducted using a simple random sampling method to select patients from governmental health care clinics in Jericho Governorate. Face-to-face interviews of patients were accompanied using a structured questionnaire asking for risk factors of infection with Parasitic diseases. Fresh feces specimens were examined macroscopically for the presence of adult worms and microscopically for parasites ova, cysts, oocysts, and/or larvae to determine intestinal parasitic infection. The statistical package for social science (SPSS) program, version 22 for windows, was used for data entry and data analysis Appropriate statistical tests (parametric or nonparametric) were selected based on the nature of the data, whether qualitative or quantitative. A P-value equal to or below 0.05 was considered significant.
Results/Discussion: The results of the study provide insight into the demographic characteristics of the 495 patients who participated from the Governmental Primary Health Care Clinics in Jericho. The study included 234 male patients (47.3%) and 261 female patients (52.7%). The patients' residences were distributed as follows: 352 (71.1%) living in the city, 115 (23.2%) in a village, and 28 (5.7%) in a camp. By analyzing the participants' responses to various risk factors and hygiene practices, these possible factors might contribute to the spread of gastroenteritis. There is a significant positive correlation between the incidence of parasitic infections and the presence of family complaints related to abdominal pain, diarrhea, or medication intake. Moreover, a significant negative correlation was observed between the incidence of parasitic infections and the habit of washing hands after meals. The occurrence of bloody diarrhea was found to be significantly correlated with parasitic infections. Some factors do not exhibit a significant impact on parasitic infection prevalence, like wearing shoes and working with animals. The correlation between parasitic infections and clinical symptoms emphasizes the need for early detection and prompt medical treatment to prevent complications.الخلفية: تنتشر العدوى الطفيلية المعوية (IPI) على مستوى العالم، وخاصة ملايين الأشخاص في المناطق الفقيرة مصابون بمرض الديدان الطفيلية وخاصة أطفال المدارس. وزارة الصحة الفلسطينية تقوم سنويا بالإبلاغ عن عدة حالات من الإصابة بالطفيليات المعوية، بما في ذلك داء الجيارديا، وداء الصفر، وداء المعوية، وداء الأندويدات، وداء الزخار. العوامل المرتبطة بالوضع الاجتماعي والاقتصادي المنخفض، ونقص الوعي بالنظافة الشخصية، والتلوث البيئي هي أكبر عوامل الخطر المثيرة للعدوى المعوية بالديدان المعوية. بسبب العدوى الطفيلية، يعتبر فقر الدم متعدد العوامل، والذي يرتبط في المقام الأول بنقص الحديد ونقص التغذية عند الأطفال. الهدف: الهدف من الدراسة هو تقييم عوامل الخطر المرتبطة بالعدوى الطفيلية المعوية بين المرضى الذين يترددون على العيادات الحكومية للرعاية الصحية الأولية في اريحا.منهجية الدراسة: تم إجراء دراسة مقطعية باستخدام عينات عشوائية، حيث تم اختيار المرضى من المراكز الصحية الحكومية في محافظة أريحا. وتم عمل مقابلات وجها لوجه مع المرضى ووالديهم حيث انهم قاموا بتعبئة استبانة منظمة تسأل عن عوامل خطر الإصابة بالأمراض الطفيلية للفئة العمرية من سنة الى الخامسة عشرة. وبعد ذلك تم اخذ عينات للبراز من المرضى مجهريا لفحص اما وجود ديدان بالغة، او طفيليات سواء بويضات، او خراجات، أو يرقات لتحديد نوع الطفيليات المعوية. النتائج/المناقشة: توفر الدراسة نتائج على الخصائص الديموغرافية ل 495 مريضا شاركوا من عيادات الصحة الأولية الحكومية في أريحا. شملت الدراسة 234 مريضا من الذكور (47.3٪) و 261 مريضا من الإناث (52.7٪). وتوزعت مساكن المرضى على النحو التالي 352 (71.1٪) يعيشون في المدينة، و 115 (23.2٪) في قرية، و 28 (5.7٪) في مخيم. من خلال تحليل استجابات المشاركين لمختلف عوامل الخطر وممارسات النظافة، قد تساهم هذه العوامل المحتملة في انتشار التهاب المعدة والأمعاء. هناك علاقة إيجابية كبيرة بين حدوث الالتهابات الطفيلية ووجود شكاوى عائلية تتعلق بآلام البطن أو الإسهال أو تناول الدواء. علاوة على ذلك، لوحظ وجود علاقة سلبية كبيرة بين حدوث الالتهابات الطفيلية وعادة غسل اليدين بعد الوجبات. تم العثور على حدوث الإسهال الدموي ليكون مرتبطا بشكل كبير مع الالتهابات الطفيلية
Real-Time UAV Recognition Through Advanced Machine Learning for Enhanced Military Surveillance
No full textIn an era where the military utilization of Unmanned Aerial Vehicles (UAVs) has become essential for surveillance and operational operations, our study tackles the growing demand for real-time, accurate UAV recognition. The rise of UAVs presents numerous safety hazards, requiring systems that distinguish UAVs from non-threatening phenomena, such as birds. This research study conducts a comparative examination of advanced machine learning models, aiming to address the challenge of real-time aerial classification in diverse environmental conditions without model retraining. This research employs extensive datasets to train and validate models such as Neural Networks, Support Vector Machines, ensemble methods, and Gradient Boosting Machines. The fashions are evaluated based on accuracy, forgetfulness, and processing efficiency—criteria determining the viability of real-time operational scenarios. The findings indicate that Neural Networks exhibit enhanced performance, demonstrating exceptional accuracy in distinguishing UAVs from birds. This culminates in our primary assertion: Neural Networks possess vital operational security ramifications and can markedly enhance the allocation of defense resources. The findings significantly improve surveillance systems, highlighting the effectiveness of machine-learning methods in real-time UAV identification. Moreover, incorporating Neural Network systems into military defenses is recommended to enhance decision-making capabilities and security operations. Foresee forthcoming UAV developments and advocate for regular model updates to keep up with increasingly nimble and perhaps stealthier drone designs
Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction
Get PDFOral cancer presents a formidable challenge in oncology, necessitating early diagnosis and accurate prognosis to enhance patient survival rates. Recent advancements in machine learning and data mining have revolutionized traditional diagnostic methodologies, providing sophisticated and automated tools for differentiating between benign and malignant oral lesions. This study presents a comprehensive review of cutting-edge data mining methodologies, including Neural Networks, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and ensemble learning techniques, specifically applied to the diagnosis and prognosis of oral cancer. Through a rigorous comparative analysis, our findings reveal that Neural Networks surpass other models, achieving an impressive classification accuracy of 93.6% in predicting oral cancer. Furthermore, we underscore the potential benefits of integrating feature selection and dimensionality reduction techniques to enhance model performance. These insights underscore the significant promise of advanced data mining techniques in bolstering early detection, optimizing treatment strategies, and ultimately improving patient outcomes in the realm of oral oncolog
AI in the Sky: Developing Real-Time UAV Recognition Systems to Enhance Military Security
Get PDFIn an era where Unmanned Aerial Vehicles (UAVs) have become crucial in military surveillance and operations, the need for real-time and accurate UAV recognition is increasingly critical. The widespread use of UAVs presents various security threats, requiring systems that can differentiate between UAVs and benign objects, such as birds. This study conducts a comparative analysis of advanced machine learning models to address the challenge of aerial classification in diverse environmental conditions without system redesign. Large datasets were used to train and validate models, including Neural Networks, Support Vector Machines, ensemble methods, and Random Forest Gradient Boosting Machines. These models were evaluated based on accuracy and computational efficiency, key factors for real-time application. The results indicate that Neural Networks provide the best performance, demonstrating high accuracy in distinguishing UAVs from birds. The findings emphasize that Neural Networks have significant potential to enhance operational security and improve the allocation of defense resources. Overall, this research highlights the effectiveness of machine learning in real-time UAV recognition and advocates for the integration of Neural Networks into military defense systems to strengthen decision-making and security operations. Regular updates to these models are recommended to keep pace with advancements in UAV technology, including more agile and stealthier design
The foreign policy of King Abdulaziz (1927-1953) : a study in the international relations of an emerging state
Get PDFKing Abdulaziz stood out as a major figure in Saudi domestics and foreign policy. He laid the foundation for Saudi foreign policy and international relations. Available
studies on King Abdulaziz's foreign policy either concentrated on earlier periods or dealt with part of his era. This study deals with the whole period of King Abdulaziz, approaches his foreign policy as a case study of a newly-emerging state and assesses the problems associated with this case.
The study is organised as follows: chapter one discusses the rise of King Abdulaziz and the Saudi achievement of a sense of statehood. Chapter two explores the problems which confront newly-emerging states in the formulation and
implementation of their foreign policy. Chapter three discusses the genesis of Saudi foreign policy structure. Chapter four focuses on Saudi Arabia's policy towards the
affairs of the Arabian Peninsula. Chapter five examines the policy of King Abdulaziz towards the Arab World. Chapter six adresses the King's policy in the area of Islamic
affairs. Chapter seven analyzes the King's relations with Britain after the Treaty of Jeddah of 1927. Chapter eight deals with Saudi policy towards the U. S.
The study hopes to provide a better understanding of the process of Saudi foreign policy making under King Abdulaziz. A major finding of this study is throwing light
on the problems experienced by Saudi Arabia as a newly-emerging state while making and implementing its foreign policy, particularly, in relation to a number of
specific and general factors underlying the making and execution of this foreign policy. In this sense the study hopes to make a modest contribution to the available
literature on King Abdulaziz's foreign policy
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