The Utilitarian Decision Making from Islamic Perspectives: Review and Settlement Attempt

Utilitarian decision making is a far-famed mechanism that alleged to model humans decisions with the help of certain functions known as utility functions. Utilities metaphorically evaluate alternatives and approximate the satisfaction afforded by each. The utilitarian model is considered the fundamental basis of several artificial intelligence techniques such as game theory, evolutionary, heuristic, and fuzzy based approaches. It has been immensely utilized to develop various intelligent systems such as Decision Support Systems (DSS), Negotiation Support Systems (NSS), Multi Agent Systems (MAS), Shopbots, and proxy bidding systems. Well known that the utilitarian model has been developed based on the hypothesis of rationality and maximization which embodied some secular norms. Islam in the other hand has been derived from diverse set of provisions in which highly upholds tender values and pays significance to moral issues. As a consequence, this paper conducts a brief comparison and deduced two commons bases and four discrepancies. In the time Islam accepts utilitarian conception and do not mind the acquisitive behaves which considered essential in life success but satirically criticizes egoistic encouragement, non-satiation, and un-prioritized preferences. The Islamic objections on the utilitarian decision not necessarily meant the solid inapplicability but a reasonable treatment out to take place in order to be Islamic legitimated. Introducing the altruistic utility supposedly drops the egoistic objection and then a satiated lexicographic utility hoped to cancel the rest of contradictions. The adoption of those treatments ought to assist in developing intelligent systems that rely on Islamic legitimated decision method which upholds the Islamic moral injunctions. It is hoped that this research will provide the impetus for researchers to propose systems that rely on Islamic legitimate decision making

Study the Effect of Silica Gel Powder on Clathrate Hydrate Formation Behavior for HFC-134a Gas.

الكلاثريت هيدرات عبارة عن جزيئات معقدة تتكون من الاتصال بين الماء والغاز عند الضغط العالي ودرجات الحرارة المنخفضة. أحد الأهداف الهامة لتكنولوجيا هيدرات الغاز هو تعزيز تكوين الهيدرات أو تقليل وقت التنوي لتشكيل الكلاثريت . تم دراسة تأثير مسحوق هلام السيليكا كمحفز على تركيبة هيدرات الغاز R-134a في تجربة نظام ايزوكوريك (حجم ثابت). من الملاحظ أن للوسط المسامي  تأثير كبير في زيادة سرعة التنوي وكذلك تحسين نمو الهيدرات. في التجربة ، تمت دراسة تأثير مسحوق هلام السيليكا لتحديد تأثيرها على تكوين وتبريد 134 هيدرات  و تم الحصول على العديد من الوظائف الموضوعية من النماذج الحركية مثل كمية الغاز المستهلكة (∆n) ومعدل النمو (r (t)) وثابت المعدل الظاهري (Kapp) وتحويل الماء إلى هيدرات. ازدادت كمية الغاز المستهلكة (∆n) في النظام الثنائي مع زيادة الضغط الأولي لتكوين الهيدرات ، وأيضا معدل نمو الهيدرات (r (t)) وزيادة تحويل الماء لزيادة الهيدرات عندما تكون هذه هي المرة الأولى التي يؤثر فيها مسحوق هلام السيليكا. على هذه الوظائف  بمتوسط ​​ حجم  نشط (900) نانومتر ، مساحة سطح (0.65) م 2 / جم ، حجم المسام 210.85 سم 3 / جم ومتوسط ​​حجم المسام (900) نانومتر الذي درس للاستخدام في التطبيقات الصناعية ومعالجة المياه. بإضافة مسحوق هلام السيليكا يتحسين نمو الهيدرات و   ذلك لانه يزيد من ذوبان غاز الهيدرات ويقلل من زاوية التلامس. بالإضافة إلى ذلك ، مسحوق هلام السيليكا يؤثر بشكل إيجابي على الاتصال الماء مع الغاز من خلال زيادة سطح التفاعل بين الغاز والماء وهذا يزيد من معدل تكوين الهيدرات.One of the important aims of gas hydrate technology is to enhance the formation of hydrate or reduction the induction time for clathrate formation. The effect of the different promoter silica gel powder on R-134a gas hydrate formation has been investigated in the isochoric system experiment. It is noted that the purse media have a significant effect in increasing the speed of nucleation as well as improving the growth of hydrate. In the experiment, the effect of silica gel powder was studied to determine their effect on the composition and cooling capacity of 134 hydrates. From kinetic models were obtained many objective functions such as the amount of gas consumed (∆n), the growth rate (r (t)), and conversion of the water to hydrate. The gas consumed (∆n) of binary system increased with increase initial pressure of hydrate formation, also the hydrate growth rate (r (t)) and increase conversion of water to hydrate increase  when this the first time that the effect of silica gel pweder on these functions with average active size (900) nm, BET surface area (0.65) m2/g, pore volume 210.85 cm3/g and average pore size (900) nm that studied for use in industrial applications and water treatment. The improvement of hydrate growth is marked by the addition of silica gel powder, which in turn increase the solubility of hydrate gas and reduce the contact angle. In addition,   silica gel powder positively the contact with the gas through the increase of the interaction surface between gas and water and this increases the rate of formation of hydrate

Face Recognition System Based on Kernel Discriminant Analysis, K-Nearest Neighbor and Support Vector Machine

Although many methods have been implemented in the past, face recognition is still an active field of research especially after the current increased interest in security. In this paper, a face recognition system using Kernel Discriminant Analysis (KDA) and Support Vector Machine (SVM) with K-nearest neighbor (KNN) methods is presented. The kernel discriminates analysis is applied for extracting features from input images. Furthermore, SVM and KNN are employed to classify the face image based on the extracted features. This procedure is applied on each of Yale and ORL databases to evaluate the performance of the suggested system. The experimental results show that the system has a high recognition rate with accuracy up to 95.25% on the Yale database and 96% on the ORL, which are considered very good results comparing with other reported face recognition systems

Brain Tumor Segmentation Using Enhancement Convolved and Deconvolved CNN Model

The brain assumes the role of the primary organ in the human body, serving as the ultimate controller and regulator. Nevertheless, certain instances may give rise to the development of malignant tumors within the brain. At present, a definitive explanation of the etiology of brain cancer has yet to be established. This study develops a model that can accurately identify the presence of a tumor in a given magnetic resonance imaging (MRI) scan and subsequently determine its size within the brain. The proposed methodology comprises a two-step process, namely, tumor extraction and measurement (segmentation), followed by the application of deep learning techniques for the identification and classification of brain tumors. The detection and measurement of a brain tumor involve a series of steps, namely, preprocessing, skull stripping, and tumor segmentation. The overfitting of BTNet-convolutional neural network (CNN) models occurs after a lot of training time because training the model with a large number of images. Moreover, the tuned CNN model shows a better performance for classification step by achieving an accuracy rate of 98%. The performance metrics imply that the BTNet model can reach the optimal classification accuracy for the brain tumor (BraTS 2020) dataset identification. The model analysis segment has a WT specificity of 0.97, a TC specificity of 0.925914, an ET specificity of 0.967717, and Dice scores of 79.73% for ET, 91.64% for WT, and 87.73% for TC

Exploiting Wavelet Transform, Principal Component Analysis, Support Vector Machine, and K-Nearest Neighbors for Partial Face Recognition

Facial analysis has evolved to be a process of considerable importance due to its consequence on the safety and security, either individually or generally on the society level, especially in personal identification. The paper in hand applies facial identification on a facial image dataset by examining partial facial images before allocating a set of distinctive characteristics to them. Extracting the desired features from the input image is achieved by means of wavelet transform. Principal component analysis is used for feature selection, which specifies several aspects in the input image; these features are fed to two stages of classification using a support vector machine and K-nearest neighborhood to classify the face. The images used to test the strength of the suggested method are taken from the well-known (Yale) database. Test results showed the eligibility of the system when it comes to identify images and assign the correct face and name

K-Means clustering of optimized wireless network sensor using genetic algorithm

Wireless sensor network is one of the main technology trends that used in several different applications for collecting, processing, and distributing a vast range of data. It becomes an essential core technology for many applications related to sense surrounding environment. In this paper, a two-dimensional WSN scheme was utilized for obtaining various WSN models that intended to be optimized by genetic algorithm for achieving optimized WSN models. Such optimized WSN models might contain two cluster heads that are close to each other, in which the distance between them included in the sensing range, and this demonstrates the presence of a redundant number of cluster heads. This problem exceeded by reapplying the clustering of all sensors found in the WSN model. The distance measure was used to detect handled problem, while K-means clustering was used to redistributing sensors around the alternative cluster head. The result was extremely encouraging in rearranging the dispersion of sensors in the detecting region with a conservative method of modest number of cluster heads that acknowledge the association for all sensors nearby