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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Kamred Udham Singh Ankit Kumar Gaurav Kumar Teekam Singh Tanupriya Choudhury Ketan Kotecha School of Computing Graphic Era Hill University Dehradun India Department of Computer Engineering & Applications GLA University Mathura UP India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun CSE Dept. Symbiosis Institute of Technology Symbiosis International (Deemed University) Lavale Campus Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India

In this paper, a technique for the categorization of offline handwritten digits in the Devanagari script as well as the Roman script (English numbers) is presented. Finding out how to get the best possible recognition results while concurrently working with several scripts is the primary purpose of this study. The approach that has been described makes use of an easy profiling method for the extraction of features, together with an innovative implementation of Linear Discriminant Analysis (LDA), and a neural network architecture that has been adapted specifically for numeral classification. An exhaustive series of trials including 36,000 examples of handwritten numerals were carried out as part of the evaluation of the effectiveness of this approach. These samples were chosen at random from the CPAR datasets, with 22,000 being put to use in the construction of the training set and the remaining 14,000 being assigned to the role of the test set. In addition, the well-known MNIST database was used, which provided 60,000 samples for training purposes and 10,000 samples specifically for testing. It was determined via thorough testing that the LDA classifier produced variable results. These varying results are essential in emphasising the success of the method as well as possible areas for improvement for the method. According to the results, this strategy, which combines profile-based feature extraction with advanced classification algorithms, has the potential to lead to major breakthroughs in the area of handwritten number identification.

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Reconstructing Bayesian Networks on a Quantum Annealer

arXiv

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arXiv 2022年

作者： Zardini, Enrico Rizzoli, Massimo Dissegna, Sebastiano Blanzieri, Enrico Pastorello, Davide Department of Information Engineering and Computer Science University of Trento via Sommarive 9 Povo Trento38123 Italy Trento Institute for Fundamental Physics and Applications via Sommarive 14 Povo Trento38123 Italy

Bayesian networks are widely used probabilistic graphical models, whose structure is hard to learn starting from the generated data. O'Gorman et al. have proposed an algorithm to encode this task, i.e., the Bayesian network structure learning (BSNL), into a form that can be solved through quantum annealing, but they have not provided an experimental evaluation of it. In this paper, we present (i) an implementation in Python of O'Gorman's algorithm, (ii) a divide et impera approach that allows addressing BNSL problems of larger sizes in order to overcome the limitations imposed by the current architectures, and (iii) their empirical evaluation. Specifically, several problems with an increasing number of variables have been used in the experiments. The results have shown the effectiveness of O'Gorman's formulation for BNSL instances of small sizes, and the superiority of the divide et impera approach on the direct execution of O'Gorman's algorithm. © 2022, CC BY-NC-ND.

关键词： Bayesian networks

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Multifaceted Assessments of Traditional Chinese Word Segmentation Tool on Large Corpora 34

Multifaceted Assessments of Traditional Chinese Word Segment...

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34th Conference on Computational Linguistics and Speech Processing, ROCLING 2022

作者： Yeh, Wen-Chao Hsieh, Yu-Lun Chang, Yung-Chun Hsu, Wen-Lian Institute of Information Systems and Applications National Tsing Hua University Taiwan Graduate Institute of Data Science Taipei Medical University Taiwan Department of Computer Science and Information Engineering Asia University Taiwan Pervasive AI Research Labs Ministry of Science and Technology Taiwan

ISBN: (纸本)9789869576956

This study aims to evaluate three most popular word segmentation tool for a large Traditional Chinese corpus in terms of their efficiency, resource consumption, and cost. Specifically, we compare the performances of Jieba, CKIP, and MONPA on word segmentation, part-of-speech tagging and named entity recognition through extensive experiments. Experimental results show that MONPA using GPU for batch segmentation can greatly reduce the processing time of massive datasets. In addition, its features such as word segmentation, part-of-speech tagging, and named entity recognition are beneficial to downstream applications. © 2022 the Association for Computational Linguistics and Chinese Language Processing (ACLCLP).

关键词： Speech recognition

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More than Extracting "Important" Sentences: the Application of PEGASUS 26

More than Extracting "Important" Sentences: the Application ...

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26th International Conference on Technologies and applications of Artificial Intelligence, TAAI 2021

作者： Yang, Ting-Hao Lu, Ching-Ching Hsu, Wen-Lian National Tsing Hua University Institue of Information Systems and Applications Hsinchu City Taiwan National Tsing Hua University Institute of Taiwan Langauges and Langauge Teaching Hsinchu City Taiwan Asia University Department of Computer Science and Information Engineering Taichung City Taiwan

ISBN: (纸本)9781665408257

Pre-trained language models may reduce the amount of training data required. Among the models, PEGASUS, a recently proposed self-supervised approach, is trained to generate the pseudo-summary given the partially masked document. PEGASUS uses gap sentence generation for summarization. The most important sentences are masked, and then PEGASUS predicts the masked sentences as the output summary. In this study, however, we apply PEGASUS in a novel downstream task. We reformulate the task to generate the masked question part in a primary math word problem. In the past research, PEGASUS has shown good potentials on the few-shot datasets, so we try a smaller set of primary math text problems as well. The fine-tuning dataset sizes used in this study are 1000, 500, 50, 10 samples. Their performance are measured by a non-weighted average of the ROUGE-1, ROUGE-2, and ROUGE-L scores. The results show the outstanding performance of PEGASUS applied in our novel downstream task. © 2021 IEEE.

关键词： Natural language processing systems

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Accessible Authentication Using Keyword Search through Homomorphic Encryption

Accessible Authentication Using Keyword Search through Homom...

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Interdisciplinary Humanitarian Conference for Sustainability (IIHC), International

作者： Vipul Vekariya Manjunath. R Jasmeen Gill M Gopianand Avneesh Kumar Amaresh Jha Department of CSE PIET Parul University Vadodara Gujarat India Department of Data Science & Mathamatics Jain (Deemed to be University) Bangalore India Department of CSE RIMT University Mandi Godindgarh India Department of Computer Applications PSNA College of Engineering and Technology Dindigul Department of Computer Application Galgotias University Greater Noida Uttar Pradesh India School of Modern Media UPES Dehradun

Data storage presents substantial issues due to concerns about data security and privacy. In this study, we provide a search-based, interoperable, verified public key cryptography system. The searchability is significantly improved by our recommended method, which allows the cloud servers to create invert crypto indexes while needing a query trap and leveraging asymmetric cryptography developed by van Vries, Miller, Showing different, and Vaikuntanathan. Contrarily, the recommended method offers a brand-new permitted dataset built on the inversion decryption scheme and shows how it may be used to evaluate the thoroughness and correctness of serps. Additionally, using the provided method, many users may do encrypted searches on cryptography. Finally, it is shown that the offered technique is secure utilizing the approximate-GCD problem. The experiment's results showed that the proposed technique had less computational complexity than the methods currently in use.

关键词： Data privacy Runtime Keyword search Memory Public key cryptography Generators Servers

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Improving Smart Grid Cybersecurity by Addressing the Risk of Incorrect Data Injection

Improving Smart Grid Cybersecurity by Addressing the Risk of...

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Data science and Network Security (ICDSNS), International Conference on

作者： Muskan Yadav Bhavesh Vyas Paresh Pathak Rahul Bhatt Vaidehi Pareek Ritika Dhabliya Department of LAW Invertis University Bareilly Uttar Pradesh India Department of Electrical & Electronics Engineering School of Engineering & Technology K. R. Mangalam University Gurugram Haryana India School of Computer Science and Applications IIMT University Meerut India School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand Symbiosis Law School Symbiosis International (Deemed University) Pune India Yashika Journal Publications Private Limited India

Rapid change is being seen in the electrical grid. Microgrids, renewable energy sources, and the increased automation of grid-level decision-making made possible by sensors have all contributed to the grid's growing complexity. Studies in the field of smart grids have shown the grid's susceptibility to hacking. In example, new research shows how the insertion of erroneous data may cause a wide range of issues in the functioning of smart grids. A sophisticated assault might compromise the power grid's functioning and control without being detected by bad data detection equipment performing state estimation. In this study, we expand upon previous works in the field to detail how safeguarding selected vital sensor in the power system from fake data injection attacks might help mitigate the problem. This article discusses the IEEE 14 bus evaluation system and explains how a fake data injection attack may be carried out given just little information about the network. Different case studies are used to verify the assumptions used to determine the attacking zone. In this study, we see why protecting the electric grid from cyberattacks is so crucial.

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Wearable Technology and Cloud Computing Integration for Adaptive Cognitive Load Management

Wearable Technology and Cloud Computing Integration for Adap...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： S P Vimal Palaniraj Rajidurai Parvathy John Benito Jesudasan Peter Brinda P S. Srinivasan R Sankar Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Mphasis Corporation Chandler Arizona USA Cyber │ Data Science & Engineering Capital One Services LLC Richmond Virginia USA Department of Computer Science and Engineering Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Master of Computer Applications S.A. Engineering College (Autonomous) Chennai Tamil Nadu India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Nowadays, people must deal with more complex cognitive tasks; hence they need ways to regulate their cognitive load effectively. One potential solution to the problem of cognitive load is wearable technology, which can track physiological and behavioral data in real-time. To create adaptive cognitive load management systems, this article investigates the possibility of integrating wearable devices with cloud computing infrastructure. Wearable devices may gather data, process it, and analyze it to fine-tune cognitive load treatments in real-time by tapping into the storage and processing capacity of the cloud. Individuals' cognitive capacities and the needs of their specific situation may be considered via the proposed integration, which allows for individualized feedback and treatments. Cloud analytics also make it easy to aggregate data from several users, which is great for building adaptive algorithms and getting a better comprehensive understanding of how cognitive load patterns play out on a larger scale. Many fields might benefit greatly from this integration, such as healthcare, education, and workplace performance enhancement. To fully use cloud computing and wearable technologies for cognitive load management, however, issues including data protection, interoperability, and algorithm improvement must be resolved. Human performance and well-being may be improved, which can be achieved through multidisciplinary cooperation and technological improvements.

关键词： Productivity Cloud computing Data analysis Data protection Cognitive load Real-time systems Biomedical monitoring Wearable devices Sustainable development Interoperability

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Medical Data Classification using a Gravitational Search Algorithm and Artificial Intelligence

Medical Data Classification using a Gravitational Search Alg...

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International Conference on Smart Systems and Inventive Technology (ICSSIT)

作者： P. Dileep Kumar Reddy M. Umaselvi Devikanniga Devarajan Swati Jha Amuthaguka Raj Kumar Gupta Department of CSE Narsimha Reddy Engineering College (Autonomous) Secunderabad Telangana India Department of CSE P. A. College of Engineering and Technology Pollachi Coimbatore Tamilnadu India Department of Computer Science and Engineering Jain (Deemed-to-be) University Bangalore Karnataka India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Applications Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Physics Sardar Vallabhai Patel College Bhabua (V.K.S.U Ara)

Current years have exhibited the prosperity of several types of medical data, for the first time presenting the likelihood to expose the interior mechanism of illness through excavating the huge amount of monitored data for one such individual. Over the past decades, machine learning (ML) approaches were broadly implied for detecting distinct diseases. This enables an initial diagnosis and raises the probability of survival. Many medical data sets are unstable. Because of this, ML classification methods provide biased classification with the majority class. This manuscript derives a Gravitational Search Algorithm with Artificial Intelligence Driven Medical Data Classification (GSAAI-MDC) model. The presented technique majorly intends to accomplish reliable and accurate data classification processes in the medical sector. In order to attain this, the GSAAI-MDC technique applied Deep Support Vector Machine (DSVM) method for the classification of medical data. In addition, the GSA is utilized to enhance the performance of the DSVM method. The simulation analysis of the GSAAI-MDC technique is carried out using benchmark dataset and the results highlighted the superior outcomes of the GSAAI-MDC model over recent approaches.

关键词： Support vector machines Analytical models Machine learning algorithms Benchmark testing Data models Classification algorithms Reliability

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AI and the Future of Work in Logistics: A Delphi Study on Workforce Transformation

AI and the Future of Work in Logistics: A Delphi Study on Wo...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Shailesh Kediya Vandana Mohanty Mubina Saifee Rajani Kumar Lalit Agrawal Aarti Kulkarni School of Logistics and Supply Chain Management Symbiosis Skills and Professional University Pune India Yashaswi's IIMS Pune India Department of Business Administration Jhulelal Institute of Technology Nagpur India Green Heaven Institute of Management and Research Nagpur India Department of Computer Science and Applications Ramdeobaba University Nagpur India Central Institute of Business Management Research and Development Nagpur India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

AI being used in transportation is going to change the future of work by changing how people work together and how things are done. This Delphi study looks at how AI is changing transportation, focused on how it changes the work force and what the geopolitical effects are. This study combines ideas to shed light on how things are changing by carefully looking at the opinions of experts and predicted trends. When AI is used in transportation, big improvements are expected, such as more efficient operations, the ability to make predictions, and the ability to make decisions on its own. It is expected that these new ideas will make the supply chain more efficient, cut costs, and lower risks, making the whole system more flexible and strong. Along with these possibilities, though, there are worries about how AI will change established jobs and the need for more skills to fully use AI's potential. Key results show that experts agree on one thing: AI is the key to making all business tasks more efficient. One of the main benefits of AI is that it can automate boring chores like managing supplies, finding the best routes, and planning for future upkeep. Also, AI-powered data give you information in real time, which helps you make better decisions and adapt to changes in the market. The study finds important changes that need to happen in the workforce because of AI usage. It talks about the rise of new job categories, like AI system teachers, data scientists, and AI ethicists that need skills from a lot of different fields, like being good with technology and knowing a lot about the subject.

关键词： Ethics Technological innovation Costs Supply chains Transportation Real-time systems Stakeholders Artificial intelligence Robots Programming profession

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An analytical model for analyzing the value of information flow in the production chain model using regression algorithms and neural networks

Supply Chain Analytics

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Supply Chain Analytics 2023年 2卷

作者： Biyeme, Florent Mbakop, André Marie Chana, Anne Marie Voufo, Joseph National Advanced School of Engineering of Yaoundé (NASEY) Cameroon Laboratory of Civil and Mechanical Engineering Yaoundé Cameroon Department of Industrial and Mechanical Engineering Yaoundé Cameroon Laboratory of Mathematics Informatics Bio-informatics and Applications Yaoundé Cameroon Department of Computer Science Engineering Yaoundé Cameroon

Managing information flow has always been a challenging and critical driver of performance increase in manufacturing companies. Each bit of information related to the manufacturing process has an information flow value that can impact the process. Recent studies have focused on the traditional classification algorithms methods to analyze the value of information flow. In this research paper, we use regression algorithms to develop an analytics model for the value of information flow in manufacturing shop floors of developing countries. The analysis shows that the Artificial Neural Network (ANN) has the best regression coefficient score of 0.775 with a prediction error of 0.0125. The lowest regression coefficient score of 0.323 was for the Multi-Linear Regression (MLR) with a prediction error of 0.0556. These results help companies use regression algorithms effectively to analyze the value of information flows on the manufacturing chains. © 2023 The Authors

关键词： Artificial neural network Information flow management Production chain model Regression algorithms Value of information

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