Traffic Flow maximization is one of the crucial problems in designing a city. It directly affects the daily life of the people living in that city. It is a complex problem, one that in most cases cannot be deterministically solved. This paper proposes using evolutionary algorithms to solve that problem. This paper compares existing work and traffic flow with solutions yielded by evolutionary approach, and the results show that it is beneficial to adopt this strategy when designing traffic light timings.
White matter is one of the two components of central nervous system and consists mostly of glialcells and myelinated axons that transmits signal from one region of the cerebrum to another and between cerebrum and lower brain centers. Multiple sclerosis (MS) is one of the most common diseases which affect white matter. Multiple sclerosis is a chronic idiopathic disease resulted in multiple areas of inflammatory demyelization in the Central nervous system. MS lesion formation often leads to unpredictable cognitive decline & Physical disability. Due to the sensitivity in detecting MS lesions, MRI has become an important tool for diagnosing MS & monitoring its progression. Radiological criteria for MS include the number of lesions (some scattered bright spot) on the MRI, their location and their size. Due to the complexity & variance of automated MRI segmentation of brain MS became a complex task. A structural texture analysis method on MS segmentation scheme gives emphasis on structural analysis of MS as well as on normal tissues. An important tool that has been developed and used in variety of research is the Image Modulation model, also termed the AmplitudeModulation, Frequency-Modulation (AM-FM) image model, which models non-stationary image content using an AM-FM expansion. The AM-FM technique offers advantages for feature extraction at different frequency scales and orientations that can be used to detect different patterns, directions, or structures in an image. Thus high-frequency scale instantaneous amplitude can be used to differentiate between lesions associated with early and advanced disease stages and thus AM-FM technique can offer excellent results in classification of Multiple sclerosis from the white matter of the nervous system.
In recent years, a lot of algorithms have been proposed for the classification of the documents. Most of works done have been on English language and recently there have been works on some languages such as Chinese, Arabic, etc. In some cases, there were classifications on the Persian texts which have become essays or online projects. One of the algorithms that have been used most frequently in text Classification is KNN algorithm which is more frequently in the texts Classification in the English language. In order to use these algorithms we need suitable dataset of Persian texts, which unfortunately these data are not available to Persian Texts Classification .So the our first and second phase in this project are extracting the keywords and creating Admissible dataset for the classification of the Persian texts, and The third phase of this project is to implementing a software for the classification of the Persian texts using the extracted keywords. In this essay, we have reviewed and paid attention to some challenges of searching and classifying the Persian texts, and we have also implemented an application in order to extract the admissible dataset for the classification of the Persian texts with statistical approach or with KNN and N-gram and etc, which produces some suitable and usable dataset for the classification of the Persian texts. In the last phase we have also implemented an application in order to classify the Persian texts with a statistical approach. General Terms Natural Language Processing, Text Classification
Now-a-days the difficult to tracking the mobile devices has become an issue. Various needs are arising for finding out ways of tracking mobile devices. In this research paper included different algorithm for location tracking. The activity of tracking includes learning and inference, sensing. Different algorithms have different mechanisms based on which the tracking is made possible. For tracking a device in nearby place efforts required are less than from far-away places. Algorithms include their own mechanisms for tracking the devices easily. Some of the algorithms are simple in nature while others are complex. The cost incurred for tracking devices differs when used via wireless against wired networks. Wireless technology is beneficial for tracking the devices in close areas easily. Wire-less technology such as WI-FI becomes very helpful in such cases. For tracking devices in indoor places the system named WITS (Wireless Indoor Tracking System) is used. WLAN based location tracking algorithms is categorized into two types: deterministic and probabilistic. In this paper algorithm such as Bayesian algorithm, nearest neighbor algorithm, Historybased tracking algorithm, H.M.M. (Hidden Markov model), RADAR etc. is explained. The use of wireless 802.11 frameworks is done to locate the devices. Moreover the WI-FI technology can also be used in forest area for tracking animals. WI-FI signals are much useful in places where the wired connections are not possible to set up. Despite of the use of WI-FI signals in location tracking, they are also used in tracking Bar Code stickers. Some of the WI-FI based techniques are only software based so it decreases the cost of hardware maintenance. Radio-frequency based tracking in WLAN signals has gained more and more popularity in recent years. In early days the WLAN was used to track only static devices but later on by making advances in the technology it was possible to track the moving devices as well.