Dheyaa Ahmed Ibrahim

Solving vehicle routing problem by using improved genetic algorithm for optimal solution

Abstract Context The Vehicle Routing Problem (VRP) has numerous applications in real life. It clarifies in a wide area of transportation and distribution such as transportation of individuals and items, conveyance service and garbage collection. Thus, an appropriate selecting of vehicle routing has an extensive influence role to improve the economic interests and appropriateness of logistics planning. Problem In this study the problem is as follows: Universiti Tenaga Nasional (UNITEN) has eight buses which are used for transporting students within the campus. Each bus starts from a main location at different times every day. The bus picks up students from eight locations inside the campus in two different routes and returns back to the main location at specific times every day, starting from early morning until the end of official working hours, on the following conditions: Every location will be visited once in each route and the capacity of each bus is enough for all students included in each route. Objectives Our paper attempt to find an optimal route result for VRP of UNITEN by using genetic algorithm. To achieve an optimal solution for VRP of UNITEN with the accompanying targets: To reduce the time consuming and distance for all paths. which leads to the speedy transportation of students to their locations, to reduce the transportation costs such as fuel utilization and additionally the vehicle upkeep costs, to implement the Capacitated Vehicle Routing Problem (CVRP) model for optimizing UNITEN’s shuttle bus services. To implement the algorithm which can be used and applied for any problems in the like of UNITEN VRP. Approach The Approach has been presented based on two phases: firstly, find the shortest route for VRP to help UNITEN University reduce student’s transportation costs by genetic algorithm is used to solve this problem as it is capable of solving many complex problems; secondly, identify The CVRP model is implemented for optimizing UNITEN shuttle bus services. Finding The findings outcome from this study have shown that: (1) A comprehensive listed of active GACVRP; (2) Identified and established an evaluation criterion for GACVRP of UNITEN; (3) Highlight the methods, based on hybrid crossover operation, for selecting the best way (4) genetic algorithm finds a shorter distance for route A and route B. The proportion of reduction the distance for each route is relatively short, but the savings in the distance becomes greater when calculating the total distances traveled by all buses daily or monthly. This applies also to the time factor that has been reduced slightly based on the rate of reduction in the distances of the routes.

Computer Aided Solution for Automatic Segmenting and Measurements of Blood Leucocytes Using Static Microscope Images

Blood leucocytes segmentation in medical images is viewed as difficult process due to the variability of blood cells concerning their shape and size and the difficulty towards determining location of Blood Leucocytes. Physical analysis of blood tests to recognize leukocytes is tedious, time-consuming and liable to error because of the various morphological components of the cells. Segmentation of medical imagery has been considered as a difficult task because of complexity of images, and also due to the non-availability of leucocytes models which entirely captures the probable shapes in each structures and also incorporate cell overlapping, the expansive variety of the blood cells concerning their shape and size, various elements influencing the outer appearance of the blood leucocytes, and low Static Microscope Image disparity from extra issues outcoming about because of noise. We suggest a strategy towards segmentation of blood leucocytes using static microscope images which is a resultant of three prevailing systems of computer vision fiction: enhancing the image, Support vector machine for segmenting the image, and filtering out non ROI (region of interest) on the basis of Local binary patterns and texture features. Every one of these strategies are modified for blood leucocytes division issue, in this manner the subsequent techniques are very vigorous when compared with its individual segments. Eventually, we assess framework based by compare the outcome and manual division. The findings outcome from this study have shown a new approach that automatically segments the blood leucocytes and identify it from a static microscope images. Initially, the method uses a trainable segmentation procedure and trained support vector machine classifier to accurately identify the position of the ROI. After that, filtering out non ROI have proposed based on histogram analysis to avoid the non ROI and chose the right object. Finally, identify the blood leucocytes type using the texture feature. The performance of the foreseen approach has been tried in appearing differently in relation to the system against manual examination by a gynaecologist utilizing diverse scales. A total of 100 microscope images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual segmentation method for accurately determining the ROI. We have evaluated the blood leucocytes identification using the ROI texture (LBP Feature). The identification accuracy in the technique used is about 95.3%., with 100 sensitivity and 91.66% specificity.

Artificial neural networks for automatic segmentation and identification of nasopharyngeal carcinoma

Abstract Nasopharyngeal Carcinoma (NPC) diagnostic is a challenging issue that have not been optimally solved. NPC has a complex structure which makes it difficult to diagnose even by an expert physician. Many researchers over the last few decades till now have established a lot of research efforts and used many methods with different techniques. However, the best solution to resolve the mentioned issue is very complex and needs innovative methods to find the optimal solutions. The study presents a novel automatic segmentation and identification for NPC by artificial neural networks from microscopy images without human intervention by developing the best characteristics towards preliminary NPC cases discovery. For getting accurate region of NPC in the microscope image, we propose a novel NPC segmentation method that has three major innovation points. First, K -means clustering will be used in the first stage after enhancing the image to be labelled in the regions based on their colour. Second, neural network has been employed to select the right object based on training stage. Third, texture feature for the segmented region will extract to ted to the segmentation. Regarding to the identification, the colour features have used to diagnose the ovarian tumours to the differential between benign and malignant. The findings outcome from this study have shown that: (1) A new adaptive method has been used as post-processing in detecting NPC, (2) Identified and established an evaluation criterion for automatic segmentation and identification of NPC cases, (3) Highlight the methods: based on region growing based technique and K -means clustering method for selecting the best region and (4) Assessed the efficiency of the anticipated results by associating ANN and SVM segmentation results, and automatic NPC classification. Also indicate that the texture features have some extra value or added value in separating benign from malignant. Therefore, we can use the proposed system, first, as indicator to diagnosis the case, second, use it as a support tool for the doctor to support his decision. We evaluated the effectiveness of the framework by firstly comparing the automatic segmentation against the manual, and then integrating the proposed segmentation solution into a classification framework for identifying benign and malignant tumour. Both test results show that the method is effective in segmentation the region of interest which is around 88.03% Consequently, this rate expanded to 91.01% when line presumption (NPC classification) based on ANN technique is employed with high level accuracy of classification (sensitivity) of 93.42% and specificity of 90.01%.

Automatic segmentation and automatic seed point selection of nasopharyngeal carcinoma from microscopy images using region growing based approach

Abstract Context Nasopharyngeal carcinoma (NPC) is a type of cancer in the head and neck, and this cancer presents in the throat region between the pharynx and nasal cavity. NPC is frequently detected in Southeast Asia, particularly in the southern part of China, Malaysia, Singapore, Hong Kong, Taiwan, Vietnam, and Thailand. Problem The diagnostic procedure of NPC entirely depends on the Physicians experience and involves multiple subjective decisions. Subjective decision-making can result in inter and intra observer variations. Inter-observer variation is the total difference obtained from the results of two and above observers when scrutinizing similar materials. Variation amongst the observers is the total difference an observer experience when spotting the same material many times. Tradition diagnostic of NPC has many limitations such as the time consuming for doctors to identify and recognize the tumor area slice by slice and reduce radiologists’ workloads. In addition, another challenge lies in the appearance of doctors used the observation of human eyes (human errors) in NPC cases can be missed detailed information. Approach A novel approach to automatic segmentation plus initial seed generated without human intervention of nasopharyngeal carcinoma using region growing based technique from microscopy images is presented in this study by take advantage of geometric features to detection of NPC images. In order to get accurate region of NPC image, the proposed results utilize wavelet transform for image enhancement by reduce the noise by remove the high ratio sub-bands and predestine a developed NPC image. Segmentation steps including many phases. Firstly, the thresholding is mean value used to binarise the image and secondly, filtering or remove unwanted objects in the images. Finding The findings outcome from this study have shown that: (1) a new adaptive threshold is used as a post-processing to at long last detect the NPC; (2) identified and established an evaluation criterion for automatic segmentation of NPC cases; (3) highlight the methods, based on region growing based technique and active contour operation, for selecting the best region; (4) assessed the performance of the proposed results by comparing the manual measurements and automatic NPC segmentation. The NPC segmentation rate in the technique used is about 83.89%. Comparably, this amount expanded to 92.04% once a line presumption (NPC approximation) was utilized in one of the stage in the technique here.

Solving vehicle routing problem by using improved K-nearest neighbor algorithm for best solution

Abstract Context Vehicle routing problem (VRP) is one of the many difficult issues that have no perfect solutions yet. Many researchers over the last few decades have established numerous researches and used many methods with different techniques to handle it. But, for all research, finding the lowest cost is very complex. However, they have managed to come up with approximate solutions that differ in efficiencies depending on the search space. Problem In this study the problem is as follows: have a number of vehicles which are used for transporting applications to instance place. Each vehicle starts from a main location at different times every day. The vehicle picks up applications from start locations to the instance place in many different routes and return back to the start location in at specific times every day, starting from early morning until the end of official working hours, on the following conditions: (1) Every location will be visited once in each route, and (2) The capacity of each vehicle is enough for all applications included in each route. Objectives Our paper attempt to find an optimal route result for VRP by using K-Nearest Neighbor Algorithm (KNNA). To achieve an optimal solution for VRP with the accompanying targets: (1) To reduce the distance and the time for all paths this leads to speedy the transportation of customers to their locations, (2) To implement the capacitated vehicle routing problem (CVRP) model for optimizing the solutions. Approach The approach has been presented based on two phases: firstly, the algorithms have been adapted to solve the research problem, where its procedure is different than the common algorithm. The structure of the algorithm is designed so that the program does not require a large database to store the population, which speeds up the implementation of the program execution to obtain the solution; secondly, the algorithm has proven its success in solving the problem and finds a shortest route. For the purpose of testing the algorithm’s capability and reliability, it was applied to solve the same problem online validated and it achieved success in finding a shorter route. Finding The findings outcome from this study have shown that: (1) A universal listed of dynamic KNNACVRP; (2) Identified and built up an assessment measure for KNNACVRP; (3) Highlight the strategies, based KNNA operations, for choosing the most ideal way (4) KNNA finds a shorter route for VRP paths. The extent of lessening the distance for each route is generally short, but the savings in the distance becomes more noteworthy while figuring the aggregate distances traveled by all transports day by day or month to month. This applies likewise to the time calculate that has been decreased marginally in view of the rate of reduction in the distances of the paths.

Review on Nasopharyngeal Carcinoma: Concepts, methods of analysis, segmentation, classification, prediction and impact: A review of the research literature

Abstract Context Nasopharyngeal Carcinoma (NPC) is the most famous type of tumor in the neck and started in the nasopharynx, the area at the top of the pharynx or “throat”, in which the participation of the relevant nose and tube sound including all upper respiratory tract. Purpose The study is a reviewed literature on NPC Diagnosis. The objectives of the paper are to understand; (1) The conceptual definitions of Nasopharyngeal Carcinoma, (2) the descriptive nature of the structure of the prediction NPC, (3) The contextual usage of NPC, (4) who have access to the NPC, (5) the nature and components of the data used in NPC study, (6) what are the objectives of this field of research (7) what data collection techniques were employed in the previous researches (8) what were the outcome of the previous studies. Methodology Until this time no systematic literature reviews (SLR) were conducted on NPC based on Segmentation, Classification, and Prediction. The aim of the study therefore, is to conduct a systematic review, classification and comparison of the previous methodology and approaches used in the studies on Nasopharyngeal Carcinoma based on Segmentation, Classification, and Prediction composition (published between 1970 and 2016). We therefore systematically reviewed available researches related to the NPC. We search for available literature using five electronics databases: ScienceDirect.com | Science, health and medical journals, IEEE – The worlds largest technical & professional organization, Springer – International Publisher Science, Technology, Medicine, ACM digital libraries and Google Scholar. Results The concept of NPC encompasses vast information technologies, there are few papers that dwelt on explanations on the NPC structure or the terminology used, synopsis on the various methods and technologies involved in NPC, the contents of NPC, The various findings on NPC developmental work, Data Analysis, Segmentation, Classification, Prediction and Impacts of Nasopharyngeal Carcinoma. In this study, offered an explanation for NPC defined, how structure prediction Nasopharyngeal Carcinoma described, in what context NPC used, who has access to the NPC, the component data of the NPC used and studied, what is the purpose of research in this field, what methods of data collection was used in the review and whether the results of this study. Also an impression of all the several of methods and technologies involved in NPC, A synopsis of the details of NPC and the result that in NPC development work, segmentation of NPC, NPC diagnoses or the prognosis process.

Genetic case-based reasoning for improved mobile phone faults diagnosis

Abstract Different types of fault diagnostic applications that utilize case-based reasoning (CBR) are applied in the diagnosis process. However, CBR cannot provide solutions to unanticipated or unknown problems. Therefore, further investigation of the retrieval and revision mechanisms of CBR is essential in improving the diagnosis accuracy and precision of the method. This study proposes a hybrid scheme that combines the genetic algorithm and CBR (GCBR) to improve CBR diagnosis. CBR applies experience and knowledge on existing cases of fault diagnosis to newly provided cases. The genetic algorithm aggregates and revises relevant cases to provide solutions to unknown cases. GCBR is implemented in a mobile phone fault diagnosis application. This domain is a good testing environment because mobile phones are of various types and models. Test results show that GCBR can detect several mobile phone faults with average accuracy 98.7%.

Analysis of an electronic methods for nasopharyngeal carcinoma: Prevalence, diagnosis, challenges and technologies

Abstract Context Nasopharyngeal Carcinoma (NPC) is a category of cancer in the head and neck regions, and this cancer prevails in the throat area amongst the pharynx and nasal cavity. This NPC represent major health problem in Malaysia, being the fourth most common tumor among Malaysians and third most common cancer disease among Malaysian men. NPC is emphatically connected with Epstein-Barr Virus (EBV) and happens at the back of the nose. Objectives The overall objective of this research is to discuss the prevalence of NPC in selected regions with a focus on Asia Pacific and Malaysia in particular, the categories, severities, symptoms and the existing electronic methods of investigating NPC symptoms. The research also identifies the weaknesses and strengths of the existing methods as inputs for propose improvement. Most part of the study reviewed the current approaches and techniques that are being proposed in other literatures in order to improve the process of investigating NPC. The study critically evaluates these different methods and the problems associated with them. This study provides some review of literatures indicating relationship between cancer milestones configuration and some NPC patient’s attributes. Methods This research presents a detailed description of image based NPC, NPC imagery techniques and image types. Next, continue to NPC segmentation, which includes modelling of NPC detection, and subsequently followed by a NPC image classification process. Finally, the research paper discussing the meaning and impact of approaches and the contribution of researchers in this area. The different of NPC symptoms and their basic method of diagnosis were briefly explained. The basic diagnostic procedure is depicted with six procedures where a patient undergoes if he or she is reported with NPC. The six procedures include Trans oral mirror examination, EBV Serology, Circulating EBV DNA in plasma (Plasma DNA), Nasoendoscopy, Biopsy and NP Screen™. Finding The strengths and weaknesses of the existing methods are identified and identification model for NPC is proposed. The lesson learned from other methods were evaluated and incorporated into proposed model. This study further describes that segmentation, classification and predication of NPC based on many methods Additionally, a literature survey on segmentation, classification and predication of NPC based on real studies has been discussed.

Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images

Abstract Breast cancer is considered to be one of the most threatening issues in clinical practice. However, existing breast cancer diagnosis methods face questions of complexity, cost, human-dependency, and inaccuracy. Recently, many computerized and interdisciplinary systems have been developed to avoid human errors in both quantification and diagnosis. A computerized system can be further improved to optimize the efficiency of breast tumour identification. The current paper presents an effort to automate characterization of breast cancer from ultrasound images using multi-fractal dimensions and backpropagation neural networks. In this study, a total of 184 breast ultrasound images (72 abnormal (tumour cases) and 112 normal cases) were examined. Various setups were employed to achieve a decent balance between positive and negative rates of the diagnosed cases. The obtained results manifested in high rates of precision (82.04%), sensitivity (79.39%), and specificity (84.75%).

Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks

The accuracy of brain tumor diagnosis based on medical images is greatly affected by the segmentation process. The segmentation determines the tumor shape, location, size, and texture. In this study, we proposed a new segmentation approach for brain tissues using MR images. The method includes three computer vision fiction strategies which are enhancing images, segmenting images, and filtering out non ROI based on the texture and HOG features. A fully automatic model‐based trainable segmentation and classification approach for MRI brain tumour using artificial neural networks to precisely identifying the location of the ROI. Therefore, the filtering out non ROI process have used in view of histogram investigation to avert the non ROI and select the correct object in brain MRI. However, identification the tumor kind utilizing the texture features. A total of 200 MRI cases are utilized for the comparing between automatic and manual segmentation procedure. The outcomes analysis shows that the fully automatic model‐based trainable segmentation over performs the manual method and the brain identification utilizing the ROI texture features. The recorded identification precision is 92.14%, with 89 sensitivity and 94 specificity.