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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]

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]

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]

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]

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]

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]

The article analyzes the use of artificial neural networks in modern cryptography and cryptoanalysis. The theoretical basis of artificial intelligence is briefly described. Sources for the use of artificial neural networks in block symmetric key encryption algorithms have been studied. As an example, the structure and operation of the SM4 block encryption algorithm are described in detail. The constant values of SM4 are shown in the tables. The components of the block encryption algorithm are described in detail. The components of artificial neurons and their computational processes are shown in graphs and formulas. For the first round of the SM4 block encryption algorithm, research was conducted using artificial neural networks. The results of the experiment are given in tabular form. Based on the results of this article, artificial neural networks can be used in cryptanalysis. In performing these calculations, it is important to correctly select the activation functions and the loss functions. Optimization should also be considered. By using artificial neural networks in cryptanalysis, new methods can be developed and results obtained. [ABSTRACT FROM AUTHOR]

Recently, the world has a treasure of data; health data is one of them. Artificial intelligence (AI), especially machine learning (ML), is required to analyze these data and construct the associated innovative applications intelligently. There are several kinds of algorithms, including unsupervised, supervised, reinforcement and semi-supervised learning. Deep learning, which is component of a wider family of machine learning technologies, can also examine massive volumes of data successfully. This study aims to review various different healthcare disease prediction strategies such as single techniques, hybrid methods, and hybrid ensemble approaches in machine learning and deep learning. [ABSTRACT FROM AUTHOR]

The best job selection is enhanced with job assortment, PM selection and VV algorithms. The performance of Machine Utilization Algorithm (MUA) compared with other task scheduling algorithms In the cloud computing environment most noteworthy problem is task scheduling to augment the concert and effectiveness of cloud it is an important move. Informed search algorithms in Artificial intelligence are applied to execute the arrangement in cloud. The MUA is used to allocate the job to Practical Machine (PM) and Virtual Vehicle (VV). In the MUA the best one as first priority assortment of job is used which may relegate the concert of cloud for minimization of mean flow time with given process time and travel in the example problem [ABSTRACT FROM AUTHOR]

Informed search algorithms in Artificial intelligence are applied to execute the arrangement in cloud. The Best First Search (BFS) is used to allocate the job to Practical Machine (PM) and Virtual Vehicle (VV). In the BFS the best one as first priority assortment of job is used which may relegate the concert of cloud for minimization of Operational Completion Time (OCT). In the cloud computing environment most noteworthy problem is task scheduling to augment the concert and effectiveness of cloud it is an important move The best job selection is enhanced with job assortment, PM selection and VV algorithms. The performance of BFS compared with other task scheduling algorithms. [ABSTRACT FROM AUTHOR]

This article provides a complete classification of attacks using artificial intelligence. Three main identified sections were considered: attacks on information systems and computer networks, attacks on artificial intelligence models (poisoning attacks, evasion attacks, extraction attacks, privacy attacks), attacks on human consciousness and opinion (all types of deepfake). In each of these sections, the mechanisms of attacks were identified and studied, in accordance with them, the methods of protection were set. In conclusion, a specific example of an attack using a pretrained model was analyzed and protected against it using the method of modifying the input data, namely, image compression in order to get rid of extraneous noise. [ABSTRACT FROM AUTHOR]