Design of infrasound-detection system via adaptive LMSTDE algorithm

A proposed solution to an aviation safety problem is based on passive detection of turbulent weather phenomena through their infrasonic emission. This thesis describes a system design that is adequate for detection and bearing evaluation of infrasounds. An array of four sensors, with the appropriate hardware, is used for the detection part. Bearing evaluation is based on estimates of time delays between sensor outputs. The generalized cross correlation (GCC), as the conventional time-delay estimation (TDE) method, is first reviewed. An adaptive TDE approach, using the least mean square (LMS) algorithm, is then discussed. A comparison between the two techniques is made and the advantages of the adaptive approach are listed. The behavior of the GCC, as a Roth processor, is examined for the anticipated signals. It is shown that the Roth processor has the desired effect of sharpening the peak of the correlation function. It is also shown that the LMSTDE technique is an equivalent implementation of the Roth processor in the time domain. A LMSTDE lead-lag model, with a variable stability coefficient and a convergence criterion, is designed

A Fuzzy Dynamic Programming for the Optimal Allocation of Health Centers in some Villages around Baghdad

دائرة التخطيط وتنمية الموارد في وزارةالصحة مهتمة جدا فيتحسينالرعاية الطبية، الثقافة الصحية وبرامج التدريب للقرى وعلى هذا الأساس وضمن الإمكانيات المتاحة للوزارة فإنها ترغب في تخصيص سبعة مراكز صحية لاربعقرىفي بغداد، العراق، لذلك فإن الوزارةتحتاج إلىتحديد عددالمراكز الصحية المخصصة لكل واحدة من تلك القرىوالذي يحقق أعظم قدر من الفعاليةالإجمالية للمراكز الصحية السبعة في بيئة ضبابية.الهدفمن هذه الدراسةهو استعمال أسلوب البرمجة الديناميكية الضبابية لتحديدالتخصيص الأمثل لهذه المراكز والذي يسمح بتحقيق أعظم قدرمن الفعاليةالإجمالية لهذهالمراكز الصحية والمتمثلة بالزيادة المتوقعة في متوسط العمر بالسنوات لسكان القريةفي بيئة ضبابية. وقدأثبتت نتائج الدراسة بعد أن تم حلمشكلة من واقع الحياة أن الأسلوب المقترحهو نموذجحسابيفعَال جدالصنعسلسلة منالقراراتالمترابطة، كماأنه يزودنا بإجراءمنهجيلتحديدالتوليفة المثلىمنالقرارات.The Planning and Resource Development Department of the Iraqi Ministry of Health is very interested in improving medical care, health education, and village training programs. Accordingly, and through the available capabilities of the ministry, itdesires to allocate seven health centers to four villages in Baghdad, Iraq therefore the ministry needs to determine the number of health centers allocated to each of these villages which achieves the greatest degree of the overall effectiveness of the seven health centers in a fuzzy environment. The objective of this study is to use a fuzzy dynamic programming(DP) method to determine the optimal allocation of these centers, which allows the greatest overall effectiveness of these health centers to be achieved, which is the expected increase in the average life years in the village population in a fuzzy environment. The results of this studyareproved after a real-life problem was solved that the proposed method is an effective mathematical model for making a series of related decisions, and it provides us with a systematic procedure to determine the optimal combination of decisions

A plan for transportation and distribution the products based on multi-objective travelling salesman problem in fuzzy environmental

Transportation and distribution are the most important elements in the work system for any company, which are of great importance in the success of the chain work. Al-Rabee factory is one of the largest ice cream factories in Iraq and it is considered one of the most productive and diversified factories with products where its products cover most areas of the capital Baghdad, however, it lacks a distribution system based on scientific and mathematical methods to work in the transportation and distribution processes, moreover, these processes need a set of important data that cannot in any way be separated from the reality of fuzziness industrial environment in Iraq, which led to use the fuzzy sets theory to reduce the levels of uncertainty. The decision-maker has several goals that he aspires to accomplish for two stages, so, the decision-maker adopted in his work system on a multi-objective travelling salesman problem. A network of paths for transportation and distribution of the products has been designed based on a multi-objective travelling salesman problem, by building a mathematical model that finds the best paths for each stage, taking into account the goals required by the decision-maker. The results obtained from the use of (Lingo) software showed the importance of these methods in determining the optimal path for the processes of collecting and transporting milk from their collection centers to the Al-Rabee factory as a first stage, as well as transporting the final products and distributing them from the Al-Rabee factory to the shopping centers as a second stage

An integrated model for solving production planning and production capacity problems using an improved fuzzy model for multiple linear programming according to Angelov's method

Decision making has become a part of our everyday lives. The main apprehension is that almost all decision difficulties include certain criteria, which usually can be multiple or conflicting. Certainly, the production planning and production capacity development includes several parameters uncertainty such as fuzzy resource capacity, fuzzy demand and fuzzy production rate. This situation makes decision maker challenging to describe the objective crisply and at the end the real optimum solution cannot attained correctly. The Fuzzy model for multi-objective linear programming should be an suitable approach for dealing with the production planning and production capacity (PP& PC) problems. The PP& PC problem based on the fuzzy environment becomes even more sophisticated as decision makers try to consider multi-objectives, Therefore, this study attempts to propose a novel scheme which is capable of dealing with these obstacles in PP& PC problem. Intuitionistic Fuzzy Optimization (1FO) by implementing the optimization problem in an Intuitionistic Fuzzy Set (IFS) environment and considered the degrees of rejection of objective(s) and of constraints as the complement of satisfaction degrees. The aim of the research is to propose a new method capable of dealing with these obstacles in the PP & PC problem. It takes into account uncertainty and makes trade-offs between multiple conflicting goals simultaneously. To verify the validity of the proposed method, a case study of the fuzzy multi-objective model of the PP&PC is used. This research takes into account uncertainty and makes a comparison between multiple conflicting goals at the same time. Therefore, this study attempts to propose a new scheme which is the modified Angelov’s approach

A multi-stages multi-objective assignment for facilities layout design

Al-Ma'amun factory of the General Company for Food Products suffers from inefficient facilities layout of its production departments and warehouses of both types (warehouses of raw materials and warehouses of finished products), which causes a lot of waste of time, resources and effort in the process of transporting raw materials or even individuals, which is leads to an increase in the costs of handling materials and not providing the service in time. Therefore, this study came to solve the problem by constructing a mathematical model based on the method of multi-stage assignment to find the optimal assignment, since the management of the factory has many goals, it was necessary to use an efficient mathematical method which is goal programming. Therefore, a multi-stage multi-objectives assignment method is applied in two stages, the first of which includes the process of transporting raw materials from raw material warehouses to the production departments to carry out the manufacturing process within them, the second stage includes the transfer of finished products from the production department to the final production warehouses. After comparing the results of the proposed layout with the results of the current layout of the factory is achieved an optimum layout of the factory because of it reduced the total traveled distance by (%25) per day, it also reduced the overall time spent by (25.5%) and reduced the total volume of spent fuel by (30.7%) per day for the new layout

Antigen-Presenting Cell/Tumour Cell Hybrid Vaccines in Cancer Immunotherapy

In recent years, there has been a considerable interest in the development of immunotherapeutic approaches for treating cancers, including strategies for inducing antigen-specific cytotoxic T cells (CTLs) capable of killing tumour cells in situ. These approaches include both the active induction of CTLs by vaccination of tumour bearing patients, and the ex vivo expansion of tumour-specific CTLs for adoptive cellular transfer. One promising approach has been through the generation of hybrid cells, formed by fusion of professional antigen presenting cells (pAPCs) with tumour cells expressing relevant tumour associated antigens. Dendritic cells (DCs) represent the most potent form of pAPCs, and have been widely used in the generation of APC/tumour cell hybrid vaccines, in the context of a range of tumour types. Studies of fusion cell vaccines in animals have demonstrated not only the induction of tumour-specific CTLs, but also protection against subsequent tumour challenge and regression of established tumours. Results of clinical trials in patients have been less dramatic, but have shown the ability of hybrid vaccines to induce tumour-specific T cell responses, in some instances associated with disease stabilization or tumour regression. In addition to dendritic cell fusion vaccines, a number of non-DC fusion vaccines have been described

Analysing an Imbalanced Stroke Prediction Dataset Using Machine Learning Techniques

A stroke is a medical condition characterized by the rupture of blood vessels within the brain which can lead to brain damage. Various symptoms may be exhibited when the brain's supply of blood and essential nutrients is disrupted. To forecast the possibility of brain stroke occurring at an early stage using Machine Learning (ML) and Deep Learning (DL) is the main objective of this study. Timely detection of the various warning signs of a stroke can significantly reduce its severity. This paper performed a comprehensive analysis of features to enhance stroke prediction effectiveness. A reliable dataset for stroke prediction is taken from the Kaggle website to gauge the effectiveness of the proposed algorithm. The dataset has a class imbalance problem which means the total number of negative samples is higher than the total number of positive samples. The results are reported based on a balanced dataset created using oversampling techniques. The proposed work used Smote and Adasyn to handle imbalanced problem for better evaluation metrics. Additionally, the hybrid Neural Network and Random Forest (NN-RF) utilizing the balanced dataset by Adasyn oversampling achieves the highest F1-score of 75% compared to the original unbalanced dataset and other benchmarking algorithms. The proposed algorithm with balanced data utilizing hybrid NN-RF achieves an accuracy of 84%. Advanced ML techniques coupled with thorough data analysis enhance stroke prediction. This study underscores the significance of data-driven methodologies, resulting in improved accuracy and comprehension of stroke risk factors. Applying these methodologies to medical fields can enhance patient care and public health outcomes. By integrating our discoveries, we can enhance the efficiency and effectiveness of the public health system

Effect of fuzzy PID controller on feedback control systems based on wireless sensor network

Wireless Networked control system (WNCS) has an important in all aspects of the life and in the research fields of Engineering. In this article, a real-time implementation of the wireless feedback control system (WFCS) is performed. The stability issue in the closed-loop control system still suffer from noise, disturbances, and need careful considerations to handle it. Three cases to discover the ability of a Fuzzy PID controller to maintain better angular position control system (PCS) is addressed and controlled by a personal computer through a wireless sensor network(WSN) constructed by ZigBee platforms. The practical issues related with the design and implementation of the wireless computerized control system (WCCS) is discussed and analyzed. The simulation results carried out with Matlab/Simulink 2018b. Different parameters effect such as maximum overshoot, sampling frequency, distance and delay time have been studied. These effects on overall system performance would be discussed. Improving the efficient use of ZigBee platform for WFCS. The simulation and experimental results prove the proposed algorithm in the field of wireless control system

An Application of Using Support Vector Machine Based on Classification Technique for Predicting Medical Data Sets

© 2019, Springer Nature Switzerland AG. This paper illustrates the utilise of various kind of machine learning approaches based on support vector machines for classifying Sickle Cell Disease data set. It has demonstrated that support vector machines generate an essential enhancement when applied for the pre-processing of clinical time-series data set. In this aspect, the objective of this study is to present discoveries for a number of classes of approaches for therapeutically associated problems in the purpose of acquiring high accuracy and performance. The primary case in this study includes classifying the dosage necessary for each patient individually. We applied a number of support vector machines to examine sickle cell data set based on the performance evaluation metrics. The result collected from a number of models have indicated that, support vector Classifier demonstrated inferior outcomes in comparison to Radial Basis Support Vector Classifier. For our Sickle cell data sets, it was found that the Parzen Kernel Support Vector Classifier produced the highest levels of performance and accuracy during training procedure accuracy 0.89733, AUC 0.94267. Where the testing set process, accuracy 0.81778, the area under the curve with 0.86556