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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Emanuele Antonioni Cristiana Sanalitro Olga Capirci Alessio Di Renzo Maria Beatrice D'Aversa Domenico Bloisi Lun Wang Ermanno Bartoli Lorenzo Diaco Valentina Presutti Daniele Nardi Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy International Telematic University UNINETTUNO Rome Italy Institute for Cognitive Sciences and Technologies - National Research Council (ISTC-CNR) Rome Italy School of LIS - SILIS Group Rome Italy Dept. of Mathematics Computer Science and Economics University of Basilicata Potenza Italy LILEC University of Bologna Bologna Italy

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

The success of the interaction between the robotics community and the users of these services is an aspect of considerable importance in the drafting of the development plan of any technology. This aspect becomes even more relevant when dealing with sensitive services and issues such as those related to interaction with specific subgroups of any population. Over the years, there have been few successes in integrating and proposing technologies related to deafness and sign language. Instead, in this paper, we propose an account of successful interaction between a signatory robot and the Italian deaf community, which occurred during the Smart City Robotics Challenge (SciRoc) 2021 competition 1 . Thanks to the use of a participatory design and the involvement of experts belonging to the deaf community from the early stages of the project, it was possible to create a technology that has achieved significant results in terms of acceptance by the community itself and could lead to significant results in the technology development as well.

关键词： Deafness Social networking (online) Smart cities Sociology Collaboration Gesture recognition Organizations

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FDP-GAMLP: New Financial Distress Prediction Technique based on Genetic Algorithm and Multi-Layer Perceptron 19

FDP-GAMLP: New Financial Distress Prediction Technique based...

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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Khedr, Ahmed M. Bannany, Magdi El Saif, Abdul-Wahid A. University of Sharjah Computer Science Dept. Sharjah27272 United Arab Emirates College of Business Administration Umm Al Quwain University Umm Al Quwain356 United Arab Emirates Ain Shams University Faculty of Business Cairo11566 Egypt King Fahd University of Petroleum & Minerals Control and Instrumentation Engineering Department Dhahran Saudi Arabia

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

Developing a reliable financial distress prediction model has been a long-standing research area. Recently, machine learning algorithms have been increasingly popular in the area. In this paper, a hybrid approach of Genetic Algorithm (GA) and Multi-Layer Perceptron (MLP) for Financial Distress Prediction (FDP) (FDP-GAMLP) is proposed. FDP-GAMLP emphasizes on GA-based tuning of the four major hyper-parameters, namely network width, network dept., network optimizer, and dense layer activation function, which can influence on whether the algorithm explodes or converges. An improved GA is utilized to optimize the MLP model's hyper-parameters for better prediction. The prediction performance is evaluated using real data set with samples of companies from countries in Middle East region. The resampling technique using k-fold evaluation metrics is adopted to get unbiased and most accurate results. The simulation results show that FDP-GAMLP outperforms the classical machine learning models in terms of predictive accuracy. © 2022 IEEE.

关键词： Genetic algorithms

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Challenges in developing and deploying AI in the engineering, procurement and construction industry

Challenges in developing and deploying AI in the engineering...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Rimma Dzhusupova Jan Bosch Helena Holmströ m Olsson Dept. of Electrical &#x0026 Instrumentation Control &#x0026 Safety Systems McDermott The Hague The Netherlands Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Department of Computer Science and Media Technology Malm&#x00F6 University Malm&#x00F6 Sweden

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

AI in the engineering, Procurement and Construction (EPC) industry has not yet a proven track record in large-scale projects. Since AI solutions for industrial applications became available only recently, deployment experience and lessons learned are still to be built up. Several research papers exist describing the potential of AI, and many surveys and white papers have been published indicating the challenges of AI deployment in the EPC industry. However, there is a recognizable shortage of in-dept. studies of deployment experience in academic literature, particularly those focusing on the experiences of EPC companies involved in large-scale project execution with high safety standards, such as the petrochemical or energy sector. The novelty of this research is that we explore in detail the challenges and obstacles faced in developing and deploying AI in a large-scale project in the EPC industry based on real-life use cases performed in an EPC company. Those identified challenges are not linked to specific technology or a company's know-how and, therefore, are universal. The findings in this paper aim to provide feedback to academia to reduce the gap between research and practice experience. They also help reveal the hidden stones when implementing AI solutions in the industry.

关键词： Industries Procurement Companies Manuals Petrochemicals Software Safety

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Regional Vegetation Cover Prediction Using Spatial Image Features and Semantic Segmentation

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SSRN 2022年

作者： Chirkhare, Ganesh Hablani, Ramchand Balamwar, Sanjay Dept. of Computer Science and Engg. Shri Ramdeobaba College of Engineering & Management Nagpur440013 India Maharashtra Remote Sensing Application Centre Nagpur440011 India

Vegetation indices play a vital role in monitoring dissimilarity in vegetation. This paper presents a better procedure for the purpose of analysis and representation of multi-spectral satellite images based upon Atmospherically Resistant Vegetation Index (ARVI) and U-Net. The vegetation detection methods were applied to color images which consist of R, G, B and near-infrared (NIR) bands. Common approaches usually apply the Normalized Difference Vegetation Index (NDVI) to indicate the greenery of environment. However, there are some potential errors and inaccuracies with NDVI, one of the most important imperfection is that it is incorrect for changes in solar incidence angle, this method also produces inaccurate data due to varying factors including shadowing, air moisture, and variations in the soil. It has been found that using ARVI and deep learning helps in eliminating these errors up to some extent, and correct the inaccuracies of NDVI, which results in slight improved and near accurate vegetation segmentation. This method can be utilized for prediction of vegetation in a region for the purpose of city planning and reforestation. © 2022, The Authors. All rights reserved.

关键词： Vegetation

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Role of Online Shopping Websites in Consumer Satisfaction Based Statistical Analysis

Role of Online Shopping Websites in Consumer Satisfaction Ba...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Mohit Mittal Hardeep Singh Shaifali Chauhan Swadha Gupta Knowtion GmbH Karlsruhe Germany Dept. of Computer Engineering & Technology Guru Nanak Dev University (of Affiliation) Amritsar India Prestige Institute of Management Gwalior India Deptt. Of Comp. Sci. and Engg. Thapar Institute of Engineering and Technology Patiala India

Nowadays, online shopping is very popular as there are many platforms are available. To dig into the consumer behavior for shopping there are many researchers, industries are working to provide the model to analyze consumer satisfaction. As people are searching a lot over the websites according to their interests, so the data is collected by the platforms and do analysis over it to find the consumer behaviour to improve their search and improve the platforms accordingly. Therefore, there is also need to analyze the selfcongurance, impulse buying and price sensitivity parameters which are directly or indirectly impact on the consumer satisfaction. Our aim is to develop a model to analyzes above mentioned parameters and find the relationship between each other. We also suggested some hypotheses and evaluate based statistical technique. The overall result is quiet satisfying.

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A Mobile-Health Research Framework for Colorectal Cancer Prevention: Coupling Behavioral Change with Biological Evidence

A Mobile-Health Research Framework for Colorectal Cancer Pre...

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IEEE International Conference on engineering, Technology and Innovation

作者： George Botis Stavros-Theofanis Miloulis Ioannis Vezakis Christos Androutsos Christos Fotis Zheshen Jiang Ioannis Kouris Konstantinos Bromis Marilena Tarousi Maria Haritou George K. Matsopoulos School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Tecreando B.V Amsterdam The Netherlands Dept. of Materials Science and Engineering University of Ioannina Ioannina Greece Innovation Department Protavio Ltd Athens Greece Department of Information System Management Hospital Center of University of Liège Liège Belgium

ISBN: (数字)9798350362435

ISBN: (纸本)9798350362442

Colorectal cancer (CRC) represents a significant global health challenge, ranking among the leading causes of cancer-related morbidity and mortality worldwide. However, despite its high incidence and mortality, CRC is considered a most preventable cancer through a healthy lifestyle adoption that may impede the development of colorectal adenomas and their transition into malignant states. In this regard, although the notions of healthy living and behavioral pathways are well-known, population adherence and awareness in relation to cancer prevention are lacking. Moreover, the evaluation of their impact on biological CRC-related processes and risk via quantifiable biomarkers remains under research. In this regard, this paper presents the design and implementation strategy of a multi-center clinical study investigating the potential of a risk assessment and behavioral change framework for CRC prevention, delivered via a digital solution. Particularly, the DIOPTRA mobile app will integrate accessible risk assessment and personalized behavioral recommendations to empower users in shifting towards healthier lifestyles. Key features of the app will include behavioral questionnaires, risk assessment, personalized suggestions, health literacy, and a diary function to track behaviors and mild symptoms. Upon study enrollment, participants will receive personalized assessment and recommendations, before a follow-up that will investigate adherence, effects, and quality of life. Moreover, a biomarker-based evaluation will be integrated, based on an under-development minimally-invasive screening blood test. Overall, this study aims to highlight the importance of mHealth solutions for CRC prevention on a population level, as well as to establish clinical relevance by seeking to link behavioral change with novel CRC biomarkers.

关键词： Couplings Technological innovation Prevention and mitigation Colorectal cancer Biomarkers Biology Mobile applications Blood

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Usage Behavior Analysis of Intelligent Network based Public Bicycle Rental System

Usage Behavior Analysis of Intelligent Network based Public ...

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2020 International computer Symposium, ICS 2020

作者： Chueh, Hao-En Kao, Chang-Yi Wu, Meng-Luen Chung Yuan Christian University Dept. of Information Management Taoyuan City Taiwan Soochow University Dept. of Computer Science and Information Management Taipei City Taiwan National Taiwan University of Science and Technology Dept. of Computer Science and Information Engineering Taipei City Taiwan

ISBN: (纸本)9781728192550

Many modern cities have their public bicycle rental systems. The bicycle rental system does not only provide convenience for short distance traveling but also gives an opportunity for the predictions of people in the city, and brings efficiency for government as well as enterprises. Some bicycle rental systems are connected to the Internet and the rental status of each bicycles are available, and huge amount of data are produced every day for analysis. In this research, several data mining approaches including the data processing, feature selection, frequent pattern mining are used to predict user behaviors. The data are cleaned and clustered for more accurate analysis. The processed data can be used to extract frequent patterns and built decision tree of user behaviors. According to the method proposed in this research, a full scheme for the public bicycle rental system is provided to make better strategies for government and commercial use. © 2020 IEEE.

关键词： Data mining

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Advanced Machine Learning Frameworks for Multimodal Image and Speech Signal Processing

Advanced Machine Learning Frameworks for Multimodal Image an...

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Advancement in Technology (ICONAT), International Conference for

作者： P Ramesh Naidu Saurabh Shandilya Srividya C N Shantanu Sudhir Gujar Supriya Devi Dankan Gowda V Department OF Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of Advance Computing Poornima College of Engineering Jaipur Rajasthan India Dept. of ECE Faculty of Engineering Management & Technology BGSIT Adichunchanagiri University B G Nagara India Department of CyberSecurity Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester NY Department of Mathematics Alliance University Bangalore Karnataka India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Multimodal data processing, especially the fusion of image and speech modality, is important for future human computer interface, medical applications and security surveillance. This research proposes the new machine learning approach for the efficient handling of multimodal data in which feature extraction from images and from the speech signal is obtained by employing CNN and LSTM networks, respectively. These features are combined by a hybrid fusion approach so that the resulting algorithm would be both accurate and fast. CIFAR-10 and LibriSpeech were used to assess the framework; the highest accuracy was 92% whilst the conventional methods like MDNN and EFMS could only reach 86%. Moreover, the analysis showed that the proposed method can take a processing time of 0.15 seconds to taxonomy and 0.45 seconds for speech data, thus making it preferable for real time data. These results illustrate the framework’s efficiency in predicting the multimodal input and demonstrate superiority over the prior methods.

关键词： Accuracy Surveillance Taxonomy Speech recognition Machine learning Speech enhancement Signal processing Feature extraction Data processing Real-time systems

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Modeling the Productivity of a Sugar Factory using Machine Learning Methods

Modeling the Productivity of a Sugar Factory using Machine L...

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International computer Sciences and Information Technologies (CSIT)

作者： Nataliia Lutska Lidiia Vlasenko Nataliia Zaiets Vitaliy Lysenko dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine dept. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798350334326

Relevant today is the digitalization of industrial enterprises, which is an evolutionary continuation of the introduction of MES/MOM. At the same time, an important role is played by the construction of mathematical models for various purposes, which are the main component of the Digital Twin development. Digital Twin provides a new philosophy for conducting enterprise production processes based on these models, which is aimed at improving the efficiency of both individual sectors and the enterprise as a whole. To increase the profit of the enterprise, it is necessary to ensure the planned productivity of technological processes, the final indicator of which is the yield of sugar. Taking into account that a significant number of material flows operate at the enterprise, only production flows with which the operator-technologist works are singled out in the work. The selected flows, based on expert assessments, were assigned weighted values of the impact on the overall performance of the sugar production. This made it possible to build a mathematical model that predicts the yield of sugar from input flows. The structure and parameters of this model are determined by machine learning methods. This is a feed-forward neural network MLP 7-23-23-1, which provides forecast accuracy with an error of less than 1%. The model can be used to model, correct, and predict the process conditions of an enterprise, which will increase the overall productivity of the sugar factory.

关键词： Productivity Biological system modeling Machine learning Predictive models Mathematical models Sugar industry Production facilities

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Social engineering attack types and prevention techniques- A survey

Social engineering attack types and prevention techniques- A...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Ramiz Salama Fadi Al-Turjman Shruti Bhatla Satya Prakash Yadav Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Turkey Artificial Intelligence Engineering Dept. AI and Robotics Institute Near East University Nicosia Turkey Research Center for AI and IoT Faculty of Engineering University of Kyrenia Kyrenia Turkey Department of Computer Science and Engineering Graphic Era Hill University Dehradun Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management (GLBITM) Greater Noida India

The term "Cyber Security" is getting more and more popular and important over the last few years. Since computers and most of the devices are connected to the internet, they are likely to be hacked by the hackers. In the past, this issue was not a big problem, because not every device is required to be connected to the internet or the internet was not popular as today. But the case has changed over the years. All the changes in the technology area also changed the cyber-attack models as well. With the development of technology and the change of usage of the internet over the last years cyber-attacks have become more common and popular by the hackers. Hackers have discovered that they have an opportunity to steal or earn money in a short time without having to spend too much effort. These days, the type of cyber-attacks is not the same as the ones in the past. As time passes, the cyber-attack methods are also changing and evolving. Today, hackers are using more advanced and effective cyber-attack methods compared to past years. There are many methods that are impossible to cover all of them in this article. Our main focus will be on social engineering attacks in this article. Social engineering attacks use different approaches to cyber attacking. Unlike trying to explode a victim’s social media password etc. using advanced exporting programs, algorithms or techniques, social engineering attacks focus on fooling victims into providing their data to hackers by themselves without using or implementing any password cracking, exploiting techniques etc. We will go over what social media engineering is, type of social engineering methods, what countermeasures can be used to protect from social engineering and more in this article.

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