Extending Networking Curriculum with Applied Artificial Intelligence

Artificial Intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques. They have been used to solve complicated practical problems and now a day is very popular. They can learn from examples, fault tolerant in the sense they are able to handle noisy and incomplete data and once trained can perform faster prediction and generalization. AI based systems are used mainly because of their symbolic reasoning, flexibility and explanation capabilities. This paper briefly presents the main AI techniques and outlines an application in boiler control. Master controller is required for a steam generating system having multiple boilers operating in parallel. AI technique of Fuzzy logic controller is proposed as master controller. The optimum performance of individual boilers is achieved by the master controller meeting the varying load demands of the steam system.

Artificial intelligence (AI) solutions are used to help make choices that include a high precision of choices they recommend and a deep understanding of choices, so that the chiefs can trust them. Verifiable, non-emblematic learning methods have greater perceptive accuracy. Express, fair representation information learning method produces increasingly justifiable Approach’s. Hybrid AI systems analyses the data and exploratory characteristics of approach types. The fundamental purpose of this commitment is to differentiate between a proper AI strategy for choice of assistance that produces reliable and fairoutcomes, depending on the various AI techniques, which provide an analysis of different approaches either with special or similar datasets. Comprehensibility is measured subjectively, however, by the form of learning process and the scale of the resulting representation of knowledge. We need a hybrid approach for classification using feature extraction in preprocessing of imbalanced dataset (raw dataset) by normalization for better classification, accuracy, and minimization of Error Rate for the smaller amount of training of significant dataset and lesser amount of training time for a good result. Moreover, data preprocessing play very important role in the field of machine learning, the result is more accurate if your data is cleanedotherwise some misclassification occurs which tend to be problematic in classification problems. Therefore, hybrid comparative analysis and approach is required for the selection of dataset with respect to machine learning classifier that will having different results with different data sets based on the hybrid approach to achieve maximum best results against any dataset. Input with maximum accurate result will be reproduced from our hybrid approach because this approach shows which type of classifiers should be used under what type of dataset you have, meanwhile exception of the generous fact based on results should be different among different classifiers when applied to different dataset. After that, a comparative analysis of different algorithms with different dataset has made and a comparison shows the enhanced significant generic approach with the hybrid approach, a clear result in the form of accuracy, precision, recall and f1 score shows the results against the specific techniques against the dataset and that tell us about the accuracy and rate of misclassification error. At the end we will see which machine learning algorithm improves the accuracy for which type of dataset.

Artificial Intelligence (AI) and Software Engineering (SE) have traditionally been largely independent disciplines. A common interest in developing computer systems is an area of overlap but fundamental differences in goals and objectives has resulted in little actual interaction. Limited success with chronic problems in both disciplines has forced practitioners in both fields to look beyond their local horizons in the hope of discovering new sources of impetus for further progress within their individual areas of interest. Software engineers are looking towards AI for both potential increase in software ‘power’ that AI techniques seems to promise, and exploitation of complexity-reduction strategies that AI has, of necessity, pioneered. AI practitioners, struggling with fragile demonstrations, would dearly like to import from SE tools, techniques and ‘know-how’ for the production of robust, reliable, and maintainable software systems. In this paper, I shall survey the range and scope of this two-way interaction between AI and SE.

Artificial intelligence (AI) is already part of our everyday life. Nowadays, facial recognition and autonomous driving are common applications of AI. It has now gone on to invade the field of medicine, and many studies showed promising applications of AI in radiology, pathology, endoscopy, and so on. Artificial intelligence (AI) has been a disruptive innovation in all areas of medicine including surgery. Not only has it been applied for research purpose but also AI can already provide significant solutions in clinical settings. Attention is now turning to the potential of AI in the field of endoscopic surgery. It is essential to understand how machine learning (ML) and deep learning (DL) work, understand current applications in surgery, and prepare for the future of surgery in the era of AI.

Artificial intelligence (AI) and machine learning (ML) are opening up important avenues of value creation inside organisations. Value creation will be strengthened if AI and ML are conceptualised using theoretical anchors rooted in a strong understanding of employee psychology. Organisations need to address anxieties of employees and assure them that AI and ML are aids in improving their productivity. They need to communicate to employees that AI and ML are not meant to displace employees from their jobs. Organisations face another dilemma regarding how to use AI and ML to draw upon the tacit knowledge and judgement of human actors to make more effective business decisions.

Artificial intelligence (AI) agent created with Deep Q-Networks (DQN) can defeat human agents in video games. Despite its high performance, DQN often exhibits odd behaviors, which could be immersion-breaking against the purpose of creating game AI. Moreover, DQN is capable of reacting to the game environment much faster than humans, making itself invincible (thus not fun to play with) in certain types of games. On the other hand, supervised learning framework trains an AI agent using historical play data of human agents as training data. Supervised learning agent exhibits a more human-like behavior than reinforcement learning agents because of imitating training data. However, its performance is often no better than human agents. The ultimate purpose of AI agents is to entertain human players. A good performance and a humanlike behavior are important factors of the AI agents, and both of them should be achieved simultaneously. This study proposes frameworks combining reinforcement learning and supervised learning and we call then separated network model and shared network model. We evaluated their performances by the game scores and behaviors by Turing test. The experimental results demonstrate that the proposed frameworks develop an AI agent of better performance than human agent and natural behavior than reinforcement learning agents.

Artificial intelligence (AI) has been transforming the practice of drug discovery in the past decade. Various AI techniques have been used in many drug discovery applications, such as virtual screening and drug design. In this survey, we first give an overview on drug discovery and discuss related applications, which can be reduced to two major tasks, i.e. molecular property prediction and molecule generation. We then present common data resources, molecule representations and benchmark platforms. As a major part of the survey, AI techniques are dissected into model architectures and learning paradigms. To reflect the technical development of AI in drug discovery over the years, the surveyed works are organized chronologically. We expect that this survey provides a comprehensive review on AI in drug discovery. We also provide a GitHub repository with a collection of papers (and codes, if applicable) as a learning resource, which is regularly updated.

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. It is a growing discipline that has enabled computer to undertake activities like facial recognition and game playing in a manner that, to an outsider, looks intelligent. There are many different toolsets that are called AI including neural networks, machine learning, expert systems, fuzzy logics, swarm intelligence and many others. These tools are being used in a wide variety of situations to solve a wide variety of complex problems, problems which have been difficult to solve using traditional algorithmic processes. Human intelligence and AI are sufficiently similar that tools developed to understand human intelligence can be applied to computer-based AI systems as well. Blooms Taxonomy is used to provide a definition of what constitutes intelligent computer applications. The article then considers the relationships between applications and intelligent systems.

Artificial Intelligence (AI)’s market is growing very fast all over the world, along with Deep Learning (DL) technologies requiring more and more data and speed to process them, in healthcare, agriculture, automotive, security, and in among several other industries. However, despite this AI rapid and increasing market expansion, there are numerous related challenges need to be tackled. In this paper, the purpose is to show through another structured approach derived from ARIZ algorithm and some principles coined by TrizStartup movement [1], some of the problems deep learning industries are trying to handle. This paper shows how TRIZ can be applied to fix one of those AI problems. In fact, due to DL neural networks (NN) hidden layers, their outputs are not reliable. If an error occurs, how could human reproduce the issue? Human are not able to identify activated neurons to analyse root causes of this malfunction. Therefore, reliability issue from hidden layers versus rapid AI market skyrocket, data and speed processing, generates innovation problems. In order to resolve this, principle 35-parameter changes with Althshuler Matrix, little people, Su-Field analysis, seventy-six standards solutions, have been used to generate quantum functions. The result described in this paper is a new function called QuantumReLU (QReLU), created with Quantum computer in order to extend classical activation function ReLU. It is then possible to fire neuron with activation function QReLU, and to use quantum states to identify activated neurons within hidden layers. Thus, Triz systematic approach led switching neural networks algorithms from classical to quantum computer, and therefore to build deep learning NN on quantum computer, based on the new QReLU activation function.

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques wont to solve various real-world problems. For a primer on machine learning, you'll want to read this seven-part series that I wrote. While human-like deductive reasoning, presumption, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms

Artificial intelligence (AI) is a set of technologies that allows robots to detect, understand, act, and learn at human-like levels. The vast bulk of the AI that surrounds us now is powered by ineffective AI. This sort of AI is demonstrated by IBM Watson, self-driving cars, and Apple's Siri. Machine learning includes deep learning as a subset which is commonly utilised in predictive modelling. It functions in the same manner that neurons do in our brains. Its structure and networking are also influenced by neural networks found in our brains. Since the 1950s, artificial intelligence (AI) has been applied in medicine. The use of artificial intelligence in ophthalmology is mostly focused on high-incidence illnesses such as diabetic retinopathy, glaucoma, age-related macular degeneration, and cataract. The chapter reviews research demonstrating AI enhancing medical diagnosis with a focus on diabetic retinopathy.

Artificial Intelligence (AI) is one of the essential parts of gaming industry. In this paper, we have examined how Artificial intelligence technology is effectively used to solve the problems that arise while developing a game and integrate new features to the game. In this research paper, two algorithms are examined they are, path finding of the bots and bots decision making. Few AI techniques are discussed in this paper and how these techniques are used to provide solution to the problems that occur in the games. These techniques are also taken into consideration to provide new features into the computer games. This paper aims to discuss on the Finite State Machine and A* methodologies, which helps in the decision making of a bot and also to find the path towards the goal.

Artificial Intelligence (AI) is gradually changing medical practice. With recent progress in digitized data acquisition, machine learning and computing infrastructure, AI applications are expanding into areas that were previously thought to be the exclusive domain of human experts. One such area is biobanking, which forms a natural extension for AI-driven activities, both because biobanking is a foundational activity for downstream precision medical research, as well as because biobanking can increasingly accommodate high-throughput sample-, image- and data-handling operational models. The increasing AI-driven appetite for higher volumes of data and images often necessitates the cross-border collaboration of biobanks; likely to develop to one of the major forces dictating the international biobanking collaboration in the near future. However there are significant challenges ahead at the same time: firstly key practical issues surrounding the implementation of AI into existing research and clinical workflows, including data standardization, data sharing and interoperability across multiple platforms. Secondly governance concerns such as privacy, transparency of algorithms, data interpretation and how the latter might impact patient safety. Here a high-level summary of these opportunities and challenges will be presented from the perspective of AI driving more and stronger international collaborations in biobanking.

Artificial intelligence (AI) has achieved significant progress in recent years and its applications cover a wide range of fields such as computer vision, natural language processing, autonomous driving and medical diagnosis. In industry, the rapid development of real-time-sensor measurement techniques promotes equipment surveillance and maintenance into the era of big data. It is still challenging to manually analyze this big data and establish general physical modelling by using the information hidden in these data. To this end, AI technology, such as machine learning and neural networks, has emerged as a promising tool to extract useful knowledge from measured data, and to tackle the real-time monitoring, diagnosis problems as well as enhancing the health management and reliability of modern industrial equipment. With the vision of establishing a strong link between AI and industrial equipment surveillance and maintenance, this special feature is designated to select, organize and exhibit the latest research progress on the cutting-edge research topics relevant to AI-Enabled Industrial Equipment Monitoring, Diagnosis and Health Management. Submissions for this topic in Measurement Science and Technology are open from 23 March 2022 to 30 September 2022 and contains 42 outstanding papers in above research fields.

Artificial intelligence (AI) technology–based intelligent robots are constructed using different technologies, such as Internet of Things (IoT), big data, deep learning, machine learning, neural network, and expert system. This particular type of robots can increase the work efficiency of humans and improve their quality of life. From the industry perspective, AI robots possess unlimited potential for development, and they are projected to be a 10-trillion-dollar industry. In this study, the critical technology of IoT is applied to develop a teaching module for an IoT smart home robot. Teaching and evaluation are performed through an embedded thematic-approach teaching strategy in the course named Automatic Measurement and Monitoring. This research aims to teach students how to integrate IoT technology into robot design and construction to build IoT smart home robots. This cross-disciplinary research incorporates emerging technology—integration of smart home, robot construction, and IoT technologies—into industrial education, teaching material and equipment development, and experimental teaching and evaluation. The participating students were juniors or seniors from the Department of Electrical Engineering or Electromechanical Engineering at the University of Technology.