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Artificial Intelligence (AI) is an essential component in modern games. With AI, players can feel the challenges in the game and the game will feel more real. AI has several branches, one of which is path-finding. Pathfinding is a way of finding the shortest path between two points. The main problem in path-finding is how to perform a path-finding accurately and requires fewer computational resources (CPU and memory). This paper describes the results of research that tested the A* and Hierarchical Pathfinding A* algorithms using the Unity 3D platform and the C# programming language. The graph or space to be used is a graph with 8 branch nodes. While the benchmarks used are the path generated and the processing time of an algorithm. This paper results in a conclusion that the path generated by the A* algorithm is shorter than the Hierarchical Pathfinding A* algorithm. The number of paths processed for the A* algorithm is more than the Hierarchical Pathfinding A* algorithm. The total execution time of the A* Algorithm is smaller than that of the A* Hierarchical Pathfinding Algorithm. The Hierarchical Pathfinding A* algorithm experiences spikes in execution time more often than the A* algorithm. However, if the total spike time, the Hierarchical Algorithm is less than the A* Algorithm. [ABSTRACT FROM AUTHOR]

Kidney is one of the significant organs in the human body which performs filtering out blood, balances fluid, removes the waste, maintains the level of electrolytes and hormone levels. So, any disorder or dysfunction in kidney needs to be detected on time in order to preserve life. Segmentation on kidney tumor in medical field is a critical task and many conventional methods have been employed for early prediction of kidney abnormalities but with limitations such as high cost, extended time for computation and analysis with huge amount of data. Due to all such problems, the prediction rate and accuracy has reduced considerably. In order to overcome the challenges, Artificial Intelligence (AI) technology has penetrated into the field of medicine particularly in the renal department. The evolution of AI in kidney therapies improve the process of diagnosis through several Machine Learning (ML) and Deep Learning (DL) algorithms. It has the capability of improving and influencing on the status with its capacity of learning from the massive data and apply them accordingly to differentiate on the circumstances. The storage of larger data and segmentation with AI assistance are highly helpful for the analysis of occurrence of the disease. AI algorithms have predicted the severity of tumor stages with effective accuracies. Hence, this paper provides a critical review of different AI based algorithms being used in the kidney tumor prognostication. Its numerous benefits in field of segmentation have been researched from the existing works and provides an insight on the contribution of AI in the kidney disease prediction. [ABSTRACT FROM AUTHOR]

The field of robotics research has been well developed with the combination of computers and machinery. With the advancement of artificial intelligence, mobile robots are also attracting more attention today, and path planning is a crucial foundational technology for mobile robots to complete transportation requirements and reach pertinent target areas. In an efficiency-conscious society, the single-goal planning of transmission is gradually failing to meet the needs of enterprises and factories for efficient operations, path planning that can simultaneously plan the optimal methods and reach many target points is increasingly replacing the conventional single-goal path planning. However, there are more factors to be considered in the real complex environment to face various complex road conditions, and for this reason, various single or hybrid algorithms are being optimized and solved for this kind of problem. This paper summarizes the main methods of path planning to simulate the scale of obstacles and environmental scene maps in various conditions, focusing on several basic algorithms and their hybrid algorithms for solving multi-objective path planning problems in global and local path planning, as well as their improvements and innovations on the basic algorithms. This paper's primary idea is to divide the multi-path planning process into various components and substitute each part into a suitable algorithm and model to solve it separately to accomplish the task of reaching multiple target points efficiently. [ABSTRACT FROM AUTHOR]

In the era of machine intelligence, dynamic gesture recognition is assumed to be a difficult and challenging problem because it involves the human element. Moreover, the gestures formed for a particular problem vary from person to person. Therefore, the accuracy of classification sometimes becomes inappropriate. Also, gesture recognition is affected by lag in detection; however, a negative lag is assumed to be favorable. In this paper, an attempt has been made to control the system volume through hand gesture recognition which may help in the remote operation of a device and differently-abled people. The YOLO algorithm will deep learning approach is used in this paper, which detects hand movement and its direction. Based on the direction of hand movement, the volume can be increased or decreased. [ABSTRACT FROM AUTHOR]

The paper explores how the daily lives of people with vision impairments are changed by artificial intelligence. They suffer a great deal in circumstances they are not aware of. When they go alone in town, people are worried about their safety. The overall aim of the system is to provide low-cost navigation assistance to blind people that give a sense of artificial vision by informing people of the artificial intelligence environment of objects. An ultrasound sensor is used to detect the distance between objects to the blind person to guide voice and vibration, which can be heard and felt by the blind person. The software can help identify objects in the world by using the voice command, conduct text analysis and recognize the document's text on paper. It can be an important way for blind people to communicate and encourage blind people to live independently. [ABSTRACT FROM AUTHOR]

The goal to promote human limits is for Artificial Intelligence (AI). It takes a posture on public administrations, represents the increasing availability of regaining clinical data and the rapid creation of intelligent strategies. The need to stress the need to use AI in the fight against the COVID-19 crisis. The paper outlines the main role played by Ai technologies in this unprecedented war and introduces a survey of AI methods used for multiple purposes in the fight against the outbreak of COVID-19. This paper also explains how the body temperature and coughing of the incoming person are assessed and whether the incoming person has not a protective facial mask. Should either of the above tests disqualify the participant, an alarming device invokes the local officials; the entrant may otherwise enter the premises after his/her hand has been sanitized. [ABSTRACT FROM AUTHOR]

AI-based predictive data mining algorithms play an important role in the prediction-role of the two-faceted roles of data mining (classification as the other role). Having a rigid predictive algorithm is tantamount to a successful and near-human predictive decision in knowledge discovery among databases being implemented among government, commercial and educational sectors. In this paper, three (3) of the most commonly used AI-based predictive data mining algorithms, namely: instance-based (nearest neighbor), statistical (naive Bayes), and Bayesian networks were evaluated using academic admission data in order to assess which predictive algorithm is best suited for predicting an ideal collegiate course for a student based on the student's high school grades and historical admission records. This study sought also to find out which among the four predictive algorithms can be used in terms of volume of historical data and number of data attributes. [ABSTRACT FROM AUTHOR]

The millimeter-wave (mmWave) technology suffers from several issues such as blockage by solid material and absorption by oxygen or rain. These issues cause degradation in TCP performance, where many packets are dropped. Retransmission for the packet drops will be required to receive the whole segments of data. This paper focuses on the side effects of congestion and link errors over TCP performance in the mmWave environment. This study proposes two mechanisms to enhance the conventional TCP Congestion Control Algorithm (CCA) through Additive Increase/Multiplicative Decrease (AIMD) approach. Firstly, the proposed method exploits the Additive Increase (AI) phase to stay in a stable state as long as possible. Secondly, the proposed method uses a Multiplicative Decrease (MD) phase to prevent the congestion window (cwnd) to be reduced. The proposed TCP enhancement will be compared with the default TCP-CCA. The evaluation of the proposed TCP enhancement is conducted in ns-3. The simulation results confirm that the proposed method refines cwnd up to 14% of in comparison with default TCP. [ABSTRACT FROM AUTHOR]

Artificial Intelligence(AI) is simulation of human intelligence in machines. It is programmed such that it can think as human and perform actions or take appropriate decision. In current covid-19 pandemic, its important to diagnosis more asymptomatic people to save their life. There various diseases related to thorax such as pneumonia, lung cancer, COPD(Chronic Obstructive Pulmonary Disease). Its leading death cause in world. Even fetus are also effected by pneumonia from birth times. The remote area people also can be saved by proper diagnosis on time by using CAD(Computer Assisted Detection). There is some challenges in training of algorithm in AI to give more accuracy. In this paper those issues such as class imbalance, multi task and data size are discussed with solutions for each problem. Different diseases, which look similar by radiologist can be detected in early stage. The pre-processing and finetuning of thorax x-ray is done before applying to CNN(convolutional neural network). Loss functions are calculated with proper weightage value. So that algorithm work even in small training set. [ABSTRACT FROM AUTHOR]

The paper presents comparison of the results obtained in a mechanism synthesis by means of some selected evolutionary algorithms. The optimization problem considered in the paper as an example is the dimensional synthesis of the path generating four-bar mechanism. In order to solve this problem, three different artificial intelligence algorithms are employed in this study. [ABSTRACT FROM AUTHOR]

This paper considers further development of the method for pre-design studies aimed at improving the reliability, survivability, and safety of complex mechanical systems, assembly units, and parts in accordance with the criteria of strength and resource. The Self-Organization Principle is represented as a combination of a heuristic hierarchical transdisciplinary model and metaheuristic and heuristic rules that automatically create a decision algorithm. The algorithm is determined by the object of study, by the chosen goal, and by the scientific and industrial description of the tasks. The representation of tasks as metaheuristics and heuristics allows for the use of methods and means for artificial intelligence, in particular, expert systems. Metaheuristics and heuristics are compiled by domain experts with further incorporation into the knowledge bases of the corresponding hierarchical complex of Expert Systems and Computational Modules. [ABSTRACT FROM AUTHOR]

Bayesian networks (BN) have step by step emerge as one of the essential successes in Artificial Intelligence (AI) investigation. Founding an operative BN structure is the core of the mastering and foundation and submission of BN. In these network SL, the vintage approach of using expert to build the community shape is regularly substituted with the aid of the facts gaining knowledge of structure technique. However, because of the massive quantity of possible community systems, the quest area is much big. The technique of BN studying over training facts commonly has the troubles of precision is low or complexity is high, which make the SL of fluctuate substantially from that of truth, which has a terrific stimulus on the reason and sensible application of BN. In order to overcome this difficult. In this paper, Novel Whale Optimization Algorithm for BN Structure Learning (SL) is (WO-BNS) introduced. The proposed WO-BNS has examined in reliability evaluation and fault prognosis of complex structures with actual case packages. In extra, fault prognosis is studied for figuring out essential components, essential reasons, and diagnosis routes. Experimental simulation effects display that the proposed WO-BNS SL algorithm has better shape and convergence. [ABSTRACT FROM AUTHOR]

In Computer Science, the Boolean Satisfiability Problem(SAT) is the problem of determining if there exists an interpretation that satisfies a given Boolean formula. SAT is one of the first problems that was proven to be NP-complete, which is also fundamental to artificial intelligence, algorithm and hardware design. This paper reviews the main algorithms of the SAT solver in recent years, including serial SAT algorithms, parallel SAT algorithms, SAT algorithms based on GPU, and SAT algorithms based on FPGA. The development of SAT is analyzed comprehensively in this paper. Finally, several possible directions for the development of the SAT problem are proposed. [ABSTRACT FROM AUTHOR]

The paper aims to present a design algorithm for multi quadrotor lanes in order to move towards the goal quickly and avoid obstacles in an area with obstacles. There are several problems in path planning including how to get to the goal position quickly and avoid static and dynamic obstacles. To overcome the problem, therefore, the paper presents fuzzy logic algorithm and fuzzy cell decomposition algorithm. Fuzzy logic algorithm is one of the artificial intelligence algorithms which can be applied to robot path planning that is able to detect static and dynamic obstacles. Cell decomposition algorithm is an algorithm of graph theory used to make a robot path map. By using the two algorithms the robot is able to get to the goal position and avoid obstacles but it takes a considerable time because they are able to find the shortest path. Therefore, this paper describes a modification of the algorithms by adding a potential field algorithm used to provide weight values on the map applied for each quadrotor by using decentralized controlled, so that the quadrotor is able to move to the goal position quickly by finding the shortest path. The simulations conducted have shown that multi-quadrotor can avoid various obstacles and find the shortest path by using the proposed algorithms. [ABSTRACT FROM AUTHOR]

Most of the mobile robot path planning is estimated to reach its predetermined aim through the shortest path and avoiding the obstacles. This paper is a survey on path planning algorithms of various current research and existing system of Unmanned Ground Vehicles (UGV) where their challenging issues to be intelligent autonomous robot. The focuses are some short reviews on individual papers for UGV in the known environment. Methods and algorithms in path planning for the autonomous robot had been discussed. From the reviews, we obtained that the algorithms proposed are appropriate for some cases such as single or multiple obstacles, static or movement obstacle and optimal shortest path. This paper also describes some pros and cons for every reviewed paper toward algorithms improvement for further work. [ABSTRACT FROM AUTHOR]

An adaptive cockroach swarm optimization (ACSO) algorithm is proposed in this paper to strengthen the existing cockroach swarm optimization (CSO) algorithm. The ruthless component of CSO algorithm is modified by the employment of blend crossover predator-prey evolution method which helps algorithm prevent any possible population collapse, maintain population diversity and create adaptive search in each iteration. The performance of the proposed algorithm on 16 global optimization benchmark function problems was evaluated and compared with the existing CSO, cuckoo search, differential evolution, particle swarm optimization and artificial bee colony algorithms. [ABSTRACT FROM AUTHOR]

In this paper we present a concept for an Intelligent System for Radiogram Analysis (ISAR) for welds quality inspection. Both, hardware and software solutions have been introduced in the system. The software operates with variety of scanner standards. It contains preliminary image processing (linear and nonlinear filtering algorithms) and some specialized functions, like Sauvola's tresholding or IQI detection. The aim of the ISAR system is to support a radiologist in his work. [ABSTRACT FROM AUTHOR]

In this paper, the authors suggest the automatic analysis system applied to radiographic images of welded joins involves defect identification in the weld and classification of defects that were found. The classification is done in accordance with welding norms. The shape of the crack is described by almost twenty features calculated for flaws extracted from radiograms. The fuzzy logic is used to implement linear and nonlinear pattern classifiers. The statistical methods such as PCA and factor analysis are applied in order to reduce the dimensionality of a multivariable data as well as to find most significant features and remove those mutually dependent. The algorithms are verified on real radiograms of welds obtained from shipbuilding industry. [ABSTRACT FROM AUTHOR]