What the software engineer should know about AI -- and vice versa

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) and related technologies like Data Mining, Machine Learning or Neural Networks became very popular in recent years. Many IT companies today require graduated students to understand and be able to apply these technologies. Application potential of AI is not limited only to robotics, image processing or intelligent agents, but also in engineering areas like computer networking and communication. However, on most universities, networking courses focus mainly on transmission protocols, network services and hardware design only while AI, machine learning or neural networks are taught separately. This causes a gap that emerges between AI theory and engineering approach. Thus, teachers of engineering courses are challenged how to introduce their students to an application of AI in the engineering areas, e.g., electronics, communication, embedded systems, power grids, etc. This paper shows how selected AI techniques presently used in computer networks can be incorporated into networking curriculum and demonstrated to students which extends student competencies and prepares them better into future jobs. We also present two case studies where AI techniques are applied on networking data in order to solve typical engineering problems.

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) 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) 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 the ability of a computer program or machine to think or learn that possess human-like intelligence. These computing devices use this intelligence to provide services such as speech recognition, natural language processing and identifying disease in healthcare. To work efficiently, AI requires adequate data that is used to train systems. The efficiency of any AI system depends on the availability of this data. This article is mainly focused on recent advents in the technology of Artificial Intelligence. The importance of AI in healthcare is identified and described in this report. The applications of Artificial Intelligence in healthcare such as clinical care, medical research, drug research and public healthcare are briefly discussed here. The purpose of this article is to demonstrate that artificial intelligence is being used in all domains of life and particularly in the field of healthcare. This report presents the role of Artificial Intelligence in healthcare.

Artificial intelligence (AI) is changing many aspects of education and is gradually being introduced to language education. This article reviews the literature to examine main trends and common findings in relation to AI technologies and applications for second and foreign language learning and teaching. With special reference to computer-assisted language learning (CALL), the article explores natural language processing (NLP), data-driven learning (DDL), automated writing evaluation (AWE), computerized dynamic assessment (CDA), intelligent tutoring systems (ITSs), automatic speech recognition (ASR), and chatbots. It contributes to discussions on understanding and using AI-supported language learning and teaching. It suggests that AI will be continuously integrated into language education, and AI technologies and applications will have a profound impact on language learning and teaching. Language educators need to ensure that AI is effectively used to support language learning and teaching in AI-powered contexts. More rigorous research on AI-supported language learning and teaching is recommended to maximise second and foreign language learning and teaching with AI.

Artificial intelligence (AI) arises from the desire of human beings to reproduce their intelligent behavior by means of computers. Recently, AI has been demonstrating remarkable success in medical image analysis owing to the rapid progress of deep learning algorithms, which have shown increasing power to solve complex real-world problems in computer vison and image analysis. The aim of this work is to introduce the main AI techniques for an interdisciplinary reader profile, specifically deep learning approaches applied nowadays in Radiology, for the intelligent diagnosis of lung cancer and respiratory diseases such as viral and bacterial pneumonia, tuberculosis and Covid-19. An overview of current techniques is presented, framed mainly in the area of deep learning and in particular convolutional neural networks. Reference to current reviews on techniques, applications, needs, software and databases is made. Then, the Deep Learning paradigm is introduced from its origins to then focus on CNNs, their main characteristics, aspects to be considered in training, representative architectures, implementation issues, including transfer learning. Finally, the software and hardware platforms widely used for the problem are described and then the concluding remarks are presented

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.

Artificial intelligence (AI) technology has become vastly more powerful and sophisticated in recent years. It remains fundamentally brittle and opaque, however, and this will limit its use in high-risk applications. If a race emerges between countries to develop and deploy cutting-edge AI, that competition will occur over both technological capabilities and over the more practical matter of whether these capabilities can be made to be trustworthy

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) based computer-aided diagnostic (CAD) systems can effectively diagnose critical disease. AI-based detection of breast cancer (BC) through images data is more efficient and accurate than professional radiologists. However, the existing AI-based BC diagnosis methods have complexity in low prediction accuracy and high computation time. Due to these reasons, medical professionals are not employing the current proposed techniques in E-Healthcare to effectively diagnose the BC. To diagnose the breast cancer effectively need to incorporate advanced AI techniques based methods in diagnosis process. In this work, we proposed a deep learning based diagnosis method (StackBC) to detect breast cancer in the early stage for effective treatment and recovery. In particular, we have incorporated deep learning models including Convolutional neural network (CNN), Long short term memory (LSTM), and Gated recurrent unit (GRU) for the classification of Invasive Ductal Carcinoma (IDC). Additionally, data augmentation and transfer learning techniques have been incorporated for data set balancing and for effective training the model. To further improve the predictive performance of model we used stacking technique. Among the three base classifiers (CNN, LSTM, GRU) the predictive performance of GRU are better as compared to individual model. The GRU is selected as a meta classifier to distinguish between Non-IDC and IDC breast images. The method Hold-Out has been incorporated and the data set is split into 90% and 10% for training and testing of the model, respectively. Model evaluation metrics have been computed for model performance evaluation. To analyze the efficacy of the model, we have used breast histology images data set. Our experimental results demonstrated that the proposed StackBC method achieved improved performance by gaining 99.02% accuracy and 100% area under the receiver operating characteristics curve (AUC-ROC) compared to state-of-the-art methods. Due to the high performance of the proposed method, we recommend it for early recognition of breast cancer in E-Healthcare.

Achieving comprehensive code quality and ensuring software reliability are critical goals in modern software engineering. This paper delves into advanced strategies that encompass both technical and organizational practices, aiming to enhance code quality and boost software reliability. Key strategies include the implementation of rigorous code review processes, adoption of automated testing frameworks, and the utilization of static and dynamic code analysis tools. Firstly, rigorous code review processes are fundamental for maintaining high standards of code quality. By fostering a collaborative environment where peer reviews are routine, teams can catch potential issues early, ensure adherence to coding standards, and promote knowledge sharing. This practice not only improves code quality but also enhances team cohesion and expertise. Secondly, automated testing frameworks are indispensable in achieving comprehensive test coverage and reducing the incidence of bugs. Unit tests, integration tests, and end-to-end tests, when automated, provide continuous feedback and ensure that code changes do not introduce new defects. This continuous testing approach is crucial for maintaining software reliability, especially in agile and DevOps environments where rapid iterations are common. Furthermore, the integration of static and dynamic code analysis tools into the development pipeline offers additional layers of quality assurance. Static analysis tools inspect the codebase for potential vulnerabilities, code smells, and compliance with coding standards without executing the program. Dynamic analysis tools, on the other hand, evaluate the software during runtime to detect memory leaks, concurrency issues, and performance bottlenecks. Together, these tools provide a comprehensive assessment of the code’s health and reliability. Moreover, fostering a culture of continuous improvement and learning within the development team is essential. Regular training sessions, workshops, and knowledge-sharing activities help in keeping the team updated with the latest advancements in software engineering practices and tools. Encouraging a mindset of quality-first and reliability among developers ensures long-term benefits and sustainability. In conclusion, achieving comprehensive code quality and ensuring software reliability require a multifaceted approach that integrates rigorous code reviews, automated testing frameworks, and both static and dynamic analysis tools. Coupled with a strong culture of continuous learning and improvement, these strategies collectively contribute to the development of robust, reliable, and high-quality software systems. Keywords: Software Reliability, Code Quality, Advanced, Strategies, Comprehensive.

Specialized tools, such as IDEs, issue trackers, and code review tools, are an indispensable part of the modern software engineering process. These tools are constantly evolving. Besides enabling tools to support a wider range of technologies and frameworks, we are learning to provide additional features in completely new ways. One prominent stream of innovation in software engineering tools is dedicated to utilizing historical data to enable data-driven features, such as defect prediction engines and recommender systems, which leverage records of prior activity to assist with decision making. Many data-driven features in software engineering tools initially get born out of the context of real-world tools as techniques devised and evaluated in synthetic settings by researchers. While convenient, synthetic evaluation of approaches that are ultimately aimed at bringing improvement to real world problems involves a number of simplifications and assumptions. In this dissertation, we highlight several aspects that, while vital for bringing innovative methods to software engineering tools, are often discarded in existing research. We closely explore several topics specific to artificial evaluation environments, such as simplifications in mining file modification histories, use of synthetic datasets for source code authorship attribution, and a gap between accuracy of reviewer recommendation models and their perception by users. Moreover, we make a case for sharing technical artifacts by converting data mining pipelines into reusable tools, and propose a novel approach to modeling expertise transfer from code modification by capturing individual contribution style of developers. Key contributions of this dissertation include a high-level model of the lifecycle of a data-driven software engineering technique, a discussion of dangerous assumptions and simplifications that are made on every step in this lifecycle, a demonstration of importance of a careful approach to mining software repositories, and a demonstration of serious misalignment between artificial evaluation and realistic environments for the problems of code reviewer recommendation and code authorship attribution. We conclude the dissertation by discussing underlying reasons for misalignment between research environments and real-world tools, and propose potential steps to narrow it down and ultimately accelerate innovation in software engineering tooling.

The work of our project is intended to help introducing digital technologies into nuclear power into nuclear power plant safety related software applications. In our project we utilize a combination of modern software engineering methods: design process discipline and feedback, formal methods, automated computer aided software engineering tools, automatic code generation, and extensive feasible structure flow path testing to improve software quality. The tactics include ensuring that the software structure is kept simple, permitting routine testing during design development, permitting extensive finished product testing in the input data space of most likely service and using test-based Bayesian updating to estimate the probability that a random software input will encounter an error upon execution. From the results obtained the software reliability can be both improved and its value estimated. Hopefully our success in the project's work can aid the transition of the nuclear enterprise into the modern information world. In our work, we have been using the proprietary sample software, the digital Signal Validation Algorithm (SVA), provided by Westinghouse. Also our work is being done with their collaboration. The SVA software is used for selecting the plant instrumentation signal set which is to be used as the input the digital Plant Protection System (PPS). This is the system that automatically decides whether to trip the reactor. In our work, we are using -001 computer assisted software engineering (CASE) tool of Hamilton Technologies Inc. This tool is capable of stating the syntactic structure of a program reflecting its state requirements, logical functions and data structure.