Other boards have been discovered in Ceylon, carved during the reign of Mahadithika Maha-Naga (9-21AD). The game is most likely an evolution of the simpler Three Mens Morris and primitive board patterns have been found dating back to as early as 1440BC, cut into the temple at Kurna, Egypt. To the best of our knowledge, we are the first who develop an agent that learns to play Seejeh game.Nine Mens Morris is another contender for the prize of 'Oldest game in the world' and is known by a number of different names in England - Nine Mens Morris or Morelles or Merrills or Merels or Mill or just plain Morris. It then plays games against itself, by combining this neural network with powerful search algorithms. The system starts with a neural network that knows nothing about the game of Seejeh. This paper presents a self-play algorithm utilizing DRL and search algorithms. In this work, we develop an automated player based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. A player might have a sequence of moves in the second stage unlike Othello and Go. Player place two tiles at each action in stage one. It has two stages Positioning and moving. different from all other strategic board games. Seejeh is a two-player, zero-sum, discrete, finite and deterministic game of perfect information. ![]() Seejeh is an ancient board game, where no one attempts to create an AI system that is able to learn to play it. DRL has been utilized as an AI computer player in many board games. Recent years have proven the existing room of deep reinforcement learning (DRL) applications. The acquired results conclude that the parallel implementation is 5X faster than the sequential version of the same operation. The processing times of both the sequential and the parallel implementations are measured to illustrate the efficiency of each implementation. Segmentation accuracy of predefined datasets and real patient datasets were the key factors for the system validation. The proposed algorithm has been validated using real medical data and simulated phantom data. Thus, a hybrid parallel implementation of FCM for extracting volume objects from medical files is proposed. Researchers state that efficiency is one of the main problems of using FCM for medical imaging when dealing with 3D models. We present a parallel implementation of the proposed algorithm using Graphics Processing Unit (GPU). In this work, we propose a modified version of FCM for segmenting 3D medical volumes, which has been rarely implemented for 3D medical image segmentation. Various extensions of it were proposed throughout the years. The 2D Fuzzy C-Means (FCM) algorithm has been extensively used for segmenting medical images due to its effectiveness. In the past, 2D models were the main models for medical image processing applications, whereas the wide adoption of 3D models has appeared only in recent years. The developed tool can be used by clinicians and medical physicians for the diagnosis and identification of neurological disorders. Experimental results demonstrate that the proposed system is quick, simple and efficient for eye tracking and saccade measurement. A pilot study is performed on ten normal participants and Multiple Sclerosis (MS) patients. ![]() ![]() Likewise, several algorithms are proposed to extract high-level eye movement saccadic measurements from the raw gaze outputs. A consistent algorithm is developed to suit the quality of the webcam using open-source software (Python) to record the time series of the eye location. This research utilizes low-resolution webcam to develop an eye tracker and saccades measurement tool to extensively lower the gadgets expenses. Eye movements can be deployed in discovering several cognitive processes of the brain. Eye movements are integrated with cognitive processes, which indeed make it a helpful research basis for the investigation of human practices.
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