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international conference on Computational Methods and data Engineering, ICMDE 2020

作者： Ojha, Yash Arora, Deepak Sharma, Puneet Tiwari, Anil Kumar Department of Computer Science & Engineering Amity University Lucknow Campus LucknowUttar Pradesh India

ISBN: (纸本)9789811568756

With the ingress of exponential advancement of Internet technologies & social media platforms, there is a potential increase that can be seen in the development of online commercial websites. With time, people started to buy goods from these websites. So, there is also a great increase in selling goods on the Internet. These sites also facilitate their customers to leave their reviews and share their experiences with other users also. These customer reviews help others to make their decision before buying that product. In other words, these reviews help to show the quality of the product. Hence for this process, mining and understanding of the reviews are very important. In this research work, authors aimed to tackle one of the natural language processing (NLP) problems, i.e., sentiment polarity classification. The authors have performed a study to compare the baseline and statistical method (machine learning) for polarity classification. This work is also intended to compare the baseline method and machine learning method, to understand which method is better and more appropriate toward sentiment classification problems with the help of Python programming. The experimental results found to be satisfactory and compared with the existing literature. © Springer Nature Singapore Pte Ltd. 2021.

关键词： Websites

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On The Understanding of Customer Purchase Behavior: A Case Study on the Computer Systems E-Commerce Website

On The Understanding of Customer Purchase Behavior: A Case S...

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international conference on Computational science and its Applications (ICCSA)

作者： Tahir Enes Adak Yunus Sahin Mounes Zaval Mehmet S. Aktas Research and Development Center Casper Computer Systems Istanbul Turkey Computer Engineering Dept. Yildiz Technical University Istanbul Turkey

The tremendous growth of the E-commerce industry over the last decade drew attention to the importance of understanding customers' behaviors and modeling user-system interactions to maximize customers' satisfaction by providing them the best user experience and gaining new customers to the e-commerce website. Within the scope of this study, software architecture _that can help us understand customers' purchase behaviors and then make a binary prediction based on their interactions with the system to determine whether a computer configuration will be bought_ is proposed and explained thor-oughly. For this purpose, machine learning algorithms are utilized to correctly model complex user-system interactions. A prototype of the proposed software architecture is developed using the latest technologies, and experimental studies are performed on top of this prototype in order to understand how successful our software architecture is. The conducted experimental results prove the efficiency of the proposed software architecture in real-world data, with 83% and 88% scores for the accuracy and F1-score metrics, respectively.

关键词： Measurement Machine learning algorithms Software architecture Scientific computing Computational modeling Prototypes Predictive models

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Editorial: Engineering applications of neurocomputing, volume II

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Frontiers in Neurorobotics 2023年 17卷 1306042页

作者： Wang, Long Song, Zhe Zhang, Zijun Huang, Chao School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China School of Business Nanjing University Nanjing China School of Data Science City University of Hong Kong Kowloon Hong Kong State Key Laboratory of Internet of Things for Smart City University of Macau China

Inspired by how the human brain works, neurocomputing algorithms, including deep learning, reinforcement learning, and neurodynamic optimization, have achieved tremendous success in various applications across many domains, e.g., visual object tracking, speech recognition, human-level control, text understanding, and real-time *** types of intelligent equipment and hardware devices are developed to implement the neurocomputing models for engineering systems. Deep learning has been employed for industrial robotic applications, including stereo reconstruction, object pose recognition, and product quality check. With the advent of Internet of Things and edge computing devices, predictive maintenance of engineering equipment is gaining popularity using deep reinforcement learning. Embedded convolutional neural networks are widely utilized for autonomous vehicle control. The success of applying neurocomputing approaches and related hardware implementations in different engineering domains, such as intelligent manufacturing, energy internet and smart healthcare, has proved the potential of employing neurocomputing for solving real problems in various engineering ***, advances in sensor and data storage technologies have enabled cumulation of a large amount of data from engineering systems. Driven by big data generated from engineering systems, neurocomputing and its hardware implementation will continually transform engineering systems to more intelligent *** research topic aims to provide a forum for researchers to present the latest research on applications of neurocomputing algorithms and neurocomputing-based hardware in engineering systems. The Volume II of this Research Topic collected another 5 high quality papers reporting the latest applications of neurocomputing in wind energy, nuclear power, and autonomous driving. The paper titled “Wind Speed Interval Prediction Based on the Hybrid Ensemble Model With Biased Convex Cost Funct

关键词： neurocomputing engineering systems neural networks manufacturing healthcare

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Transparent Blockchain-Based Voting System: Guide to Massive Deployments 6th

Transparent Blockchain-Based Voting System: Guide to Massive...

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6th international conference on Advanced Intelligent Systems and Informatics, AISI 2020

作者： Fatrah, Aicha El Kafhali, Said Salah, Khaled Haqiq, Abdelkrim Computer Networks Mobility and Modeling Laboratory: IR2M Faculty of Sciences and Techniques Hassan First University of Settat Settat26000 Morocco Electrical and Computer Engineering Department Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

ISBN: (纸本)9783030586683

With the development of society and its people democratic consciousness, voting as a crucial canal of democracy has to satisfy high expectations of modern society, new technologies and techniques must be deployed for better voting experience, and to replace traditional cost inefficient and laborious traditional paper voting. Blockchain technology is currently disrupting every industry where security and data integrity are prioritized. In this paper, we leveraged this technology to propose a design and implementation of a blockchain-based electronic voting system for large scale election. The novelty in this paper when compared to other state of the art blockchain-based voting systems is that it respects voters privacy with a full transparency for auditing and user-friendly terminals, which will boost the confidence of people in the voting system and therefor increase the number of participants in the election. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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Lopels—the ai based multi-vendor loyalty platform 1st

Lopels—the ai based multi-vendor loyalty platform

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1st international conference on Frontiers in Smart Systems technologies, ICFSST 2019

作者： James, Christy Maria Manoj, Darin Pious, Greety Margret Jose, Jaise P. Jeyakrishnan, V. Computer Science and Engineering Department Saintgits College of Engineering Kottukulam Hills Kottayam District PathamuttomKerala India

ISBN: (纸本)9789811550287

A customer loyalty program is a reward program offered by the shopkeepers to customer who frequently make purchases. Loyalty programs are one of the advanced marketing strategies that merchants adopt to influence their customers to visit back their shop again and again. But the present system is not founded to be effective in many areas such that it is not economical for small scale merchants to implement expensive loyalty system in their shops and customers need to carry a bunch of loyalty cards with them. Also, customer retention is low with the present system such that the loyalty cards do not understands customer behavior or their shopping patterns. The proposed system is an AI powered multi-vendor loyalty mobile application, where merchants can implement an economically viable loyalty system in their shops and it will be a hassle-free shopping experience for shoppers. The system uses AI technology to influence customers more to visit again to the shops by collecting their shopping data and analyzing it. So, this system will be an economically viable loyalty platform and an effective marketing tool. © Springer Nature Singapore Pte Ltd 2021.

关键词： Sales

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Neural Network Approach For Classification And Detection Of Chest Infection

Neural Network Approach For Classification And Detection Of ...

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computing and Machine Intelligence (ICMI), international conference on

作者： Mustafa Muhanad M. Salih Muhammet Çakmak Department of Computer Science and information system AL Mansour University College Baghdad Iraq Department of Electrical Electronics Engineering Karabuk University Turkey

ISBN: (数字)9781665474832

ISBN: (纸本)9781665474849

advances in computer technology have had a profound impact on our lives and the way we see the world. The healthcare industry is advancing thanks to the use of cutting-edge computer technology, which has transformed how numerous ailments are diagnosed and treated. The number of people suffering from chest-related illnesses is increasing at an alarming pace as a result of a wide range of conditions, including air pollution. Medical applications of image processing have emerged due to data collection tool development. It is now possible to make out the diagnosis through study of the features from medical investigation reports for a group of patients. That reduces the time and cost of the diagnosis, which may help plenty of people who are unable to access regular medical facilities due to intolerable cost. In this paper, automatic chest infection diagnosis is being diagnosed using a Neural Network. Two models are used, namely the Artificial Neural Network and the CNN Neural Network. The models are tested using NIH x-ray chest image data. Results are reported with 96.7% and 99.20% accuracy from the Artificial Neural Network and CNN, respectively.

关键词： Measurement Industries Costs Image processing Medical treatment Mean square error methods Convolutional neural networks

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An Empirical Evaluation of Flow Based Programming in the Machine Learning Deployment Context

An Empirical Evaluation of Flow Based Programming in the Mac...

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AI Engineering - Software Engineering for AI (WAIN), IEEE/ACM Workshop on

作者： Andrei Paleyes Christian Cabrera Neil D. Lawrence Department of Computer Science and Technology University of Cambridge United Kingdom

ISBN: (纸本)9781665452069

As use of data driven technologies spreads, software engineers are more often faced with the task of solving a business problem using data-driven methods such as machine learning (ML) algorithms. Deployment of ML within large software systems brings new challenges that are not addressed by standard engineering practices and as a result businesses observe high rate of ML deployment project failures. data Oriented Architecture (DOA) is an emerging approach that can support data scientists and software developers when addressing such challenges. However, there is a lack of clarity about how DOA systems should be implemented in practice. This paper proposes to consider Flow-Based Programming (FBP) as a paradigm for creating DOA applications. We empirically evaluate FBP in the context of ML deployment on four applications that represent typical data science projects. We use Service Oriented Architecture (SOA) as a baseline for comparison. Evaluation is done with respect to different application domains, ML deployment stages, and code quality metrics. Results reveal that FBP is a suitable paradigm for data collection and data science tasks, and is able to simplify data collection and discovery when compared with SOA. We discuss the advantages of FBP as well as the gaps that need to be addressed to increase FBP adoption as a standard design paradigm for DOA. CCS CONCEPTS • Software and its engineering → Software design tradeoffs; • computing methodologies → Machine learning.

关键词： Machine learning Computer architecture Programming data science data collection Software systems Service-oriented architecture

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Betterment of Classroom and Examination Performance Using Historical data - A Survey

Betterment of Classroom and Examination Performance Using Hi...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Aakash Rajaraman Yashas S A. Ananya V. Vivek Department of Computer Science and Engineering(Artificial Intelligence and Machine Learning) Faculty of Engineering and Technology JAIN (Deemed-to-be University)

ISBN: (数字)9781665474368

ISBN: (纸本)9781665474375

In higher education, understanding, modelling, and forecasting student performance presents considerable hurdles in terms of developing accurate and effective diagnostic models. Several researchers have attempted to construct intelligent classifiers for predicting student accomplishment, but they failed to recognise the necessity of identifying the main components that contribute to the obtained performance. Such recognition is vital for programme directors to evaluate the strengths and limitations of their academic programmes and, as a result, make the required corrections to improve student accomplishment. Consequently, there is room to present a model that improves the accuracy of predicting student academic performance, as measured by future grades in various courses, and, also an optimized multi-label classifier that predicts qualitative values for the influence of various factors on the obtained student performance, primarily using a feed-forward neural network. The goal of the experimental findings are to demonstrate significant gains over single baseline models (e.g., linear regression, matrix factorization), indicating the applicability of the suggested technique in identifying several factors influencing student performance. This study underlines the need of predicting student achievement of learning outcomes in future studies. Along with this, another model that proved to be precise and accurate in predicting the outcome in a similar but not identical scenario, was the KNN classifier, which used data to classify the graduation chances of students. Using the classifiers in the data sets, there are several analyses that can be covered in terms of question pattern, format, and more. This study aims to push a new service that can be established in several organizations and locations to benefit educational professionals and students. The datasets are naturally created in the course of the university’s classes. This allows for a lot of flexibility in terms of o

关键词： Computational modeling Linear regression Education Organizations Predictive models Libraries data models

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Expression of Concern for: Analysis of the Integration of 5G with Artificial Intelligence

Expression of Concern for: Analysis of the Integration of 5G...

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international conference on Futuristic technologies (INCOFT)

作者： Preetam Singh Gaur Deependra Rastogi Department of Life & Basic Sciences School of Life & Basic Sciences Jaipur National University Jaipur India Department of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India

ISBN: (数字)9781665450461

ISBN: (纸本)9781665450478

All as it proceeds to create and is progressively executed in a rising number of settings, this innovation will ultimately develop to where it will cause a change in outlook, moving away from the conventional traffic administration worldview and toward one that is established on a model of a natural chain that considers its variables. Integration of 5G innovation into modern areas will bring about a critical expansion in the productivity of modern result and will rush the course of carefully changing and redesigning modern areas.. The integration of 5G and man-made intelligence innovation will reshape the future instruction data climate by achieving incendiary mechanical progressions and instructive application situations. This will bring about a remade future instruction data climate. The expansion in the quantity of remote correspondences networks has gotten the way for the advancement free from state of the art advances like driverless cars, automated airplane frameworks, automated mechanical technology, the Web of Things, and augmented reality. The fifth era of remote correspondence networks is promising to convey immense information speeds, incredibly low inactivity, and exceptional constancy, which are all necessities for the advancements that require them. These mechanical headways are: (5G) Right now in the improvement of 5G advances, it is bounteously clear that there are a considerable number of troublesome and complex hubs that should be tackled. General information to take advantage of the innovations that are as of now open, an enormous number of enhancement methods and the consolidation of helper frameworks are required. The execution of artificial intelligence gives a significant open door to the goal of genuinely exceptionally confounded setup issues. It is a familiar way of thinking that man-made inte

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Dimensionality reduction and the strange case of categorical data for predicting defective water meter devices 3rd

Dimensionality reduction and the strange case of categorical...

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3rd international conference on Human Interaction and Emerging technologies: Future Applications, IHIET 2020

作者： Roccetti, Marco Casini, Luca Delnevo, Giovanni Bonfante, Simone Department of Computer Science and Engineering University of Bologna Mura Anteo Zamboni 7 Bologna40126 Italy

ISBN: (纸本)9783030553067

Further to an experiment conducted with a deep learning (DL) model, tailored to predict whether a water meter device would fail with passage of time, we came across a very strange case, occurring when we tried to strengthen the training activity of our classifier by using, besides the numerical measurements of consumed water, also other contextual available information, of categorical type. Surprisingly, that further categorical information did not improve the prediction accuracy, which instead fell down, sensibly. Recognized the problem as a case of an excessive increase of the dimensions of the space of data under observation, with a correspondent loss of statistical significance, we changed the training strategy. Observing that every categorical variable followed a quasi-Pareto distribution, we re-trained our DL models, for each single categorical variable, only on that fraction of meter devices (and corresponding measurements of consumed water) that exhibited the most frequent qualitative values for that categorical variable. This new strategy yielded a prediction accuracy level never reached before, amounting to a value of 87–88% on average. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Deep learning

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