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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Bhanusree, Y. Sree Deeksha, B. Sohan, V. Ajay, D. Eshwar Sai Ram, M. VNR VJIET Department of CSE Hyderabad India Junior Programmer Trainee Cognizant Hyderabad India Support Engineer II Amazon Hyderabad India Software Engineer NCR Corporation Hyderabad India Maters of Computer Science CSU Fresno California United States

ISBN: (纸本)9781665425773

COVID-19 is deadly contagious and with new variants forming every day, people are at great risk. 29.9 million cases have been reported in India so far and the range goes up to 179 million cases worldwide. Due to the limited number of test centers and the ever-increasing cases, the equipment and the lab technicians are getting outnumbered. They are also at constant risk of getting infected. The scientists have been working on analyzing coughs using machine learning and this technology has been successful in analyzing different types of coughs and classifying them into respective categories. Using this, a deep learning model is created that analyses the recorded cough sample and classifies them into their respective category. A CNN-Bidirectional LSTM is used to create this model and run it on the Covid-sounds dataset provided by Cambridge University. The Covid-sounds dataset has both breathing and cough samples of positive, negative, and other respiratory diseases which might alter or cause cough. This data is pre-processed and used as cough samples for the model. This model has outperformed other models which used the same dataset. © 2022 IEEE.

关键词： COVID-19

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ETHACA - A Container-Based Cyber Range for Research and Education 29

ETHACA - A Container-Based Cyber Range for Research and Educ...

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Chouliaras, Nestoras Kantzavelou, Ioanna Pantziou, Grammati Maglaras, Leandros Chouliara-Sidera, Panagiota Adamakos, Antonis Chalatsakos, Panagiotis University of West Attica Department of Informatics and Computer Engineering Athens Greece School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom University of Thessaly Department of Computer Science and Biomedical Infromatics Lamia Greece Senior Software Engineer at *** Athens Greece Open University of Cyprus Faculty of Pure and Applied Sciences Nicosia Cyprus

ISBN: (纸本)9798350377644

The advent of Industry 4.0 and the Industrial Internet of Things (IIoT) has revolutionized manufacturing and production systems, enhancing efficiency and communication through the integration of advanced technologies such as IoT devices and AI. However, this increased digitization has also led to heightened cybersecurity risks, with a notable rise in the frequency and complexity of cyber-attacks. Cyber Ranges emerges as a critical solution in addressing these challenges, offering a controlled environment for simulating cyber-attack scenarios, training cybersecurity teams, and developing robust defense strategies. The escalating demand for cybersecurity professionals has highlighted the crucial need for effective training solutions. This article presents an open-source Cyber Range constructed on container-based architecture, meticulously engineered for efficient operation with minimal resource consumption. In response to the scarcity of cybersecurity experts, this platform prioritizes extensive scalability and robust isolation between exercises, ensuring secure learning environments, and also integrates a complex developed exercise series aimed at strengthening cybersecurity learning experiences. These exercises encompass a variety of scenarios and challenges, catering to professionals at different skill levels. Each exercise replicates real-world cyber threats, offering practical, hands-on learning opportunities. Additionally, a comprehensive questionnaire accompanies these exercises, designed to gather feedback on participants' experiences. © 2024 IEEE.

关键词： Cyber attacks

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Examining the Potential of Machine Learning in Reducing Prescription Drug Costs

Examining the Potential of Machine Learning in Reducing Pres...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Sachin C. Patil Sairam Madasu Krishna J Rolla Ketan Gupta N. Yuvaraj Senior Software Engineer USA Senior Cloud Software Engineer Microsoft Charlotte North Carolina USA Department of Information Technology University of The Cumberlands Williamsburg KY USA Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The ability of the system to gain knowledge for lowering prescription drug prices has been increasingly studied in recent years. Gadget-gaining knowledge is a branch of synthetic intelligence that enables machines and PC systems to enhance their overall performance on specific obligations by means of mastering beyond facts. Within the context of prescription drug costs, gadget learning may be used to find patterns and developments in drug pricing and usage, decide disparities in getting admission to and usage, and have a look at pricing strategies hired by pharmaceutical businesses. The use of gadgets to gain knowledge can help to discover elements using prescription drug costs, inclusive of the pricing and utilization of individual capsules or the relationships among drug costs and effects. Moreover, AI-primarily based algorithms may be used to use massive datasets in an effort to apprehend the role that healthcare vendors, insurers, and pharmaceutical organizations have on prescription drug ***, AI-based total fashions can also be used to become aware of and degree cost elements associated with specific drug combinations. Sooner or later, machine studying may be leveraged to examine the effects of policy changes and healthcare reforms on prescription drug costs and to become aware of goal populations with high prescription drug prices. By combining gadget studying with conventional healthcare price analysis gear, researchers can gain insights into the drivers of prescription drug charges and make public coverage decisions. In sum, a device gaining knowledge can be a powerful tool.

关键词： Drugs Costs Accuracy Machine learning algorithms software algorithms Pricing Medical services Organizations Machine learning software

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TRANSFORMER-PATCHER: ONE MISTAKE WORTH ONE NEURON 11

TRANSFORMER-PATCHER: ONE MISTAKE WORTH ONE NEURON

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11th International Conference on Learning Representations, ICLR 2023

作者： Huang, Zeyu Shen, Yikang Zhang, Xiaofeng Zhou, Jie Rong, Wenge Xiong, Zhang State Key Laboratory of Software Development Environment Beihang University China Sino-French Engineer School Beihang University China School of Computer Science and Engineering Beihang University China Mila University of Montreal Canada WeChat AI Tencent Inc. China

Large Transformer-based Pretrained Language Models (PLMs) dominate almost all Natural Language Processing (NLP) tasks. Nevertheless, they still make mistakes from time to time. For a model deployed in an industrial environment, fixing these mistakes quickly and robustly is vital to improve user experiences. Previous works formalize such problems as Model Editing (ME) and mostly focus on fixing one mistake. However, the one-mistake-fixing scenario is not an accurate abstraction of the real-world challenge. In the deployment of AI services, there are ever-emerging mistakes, and the same mistake may recur if not corrected in time. Thus a preferable solution is to rectify the mistakes as soon as they appear nonstop. Therefore, we extend the existing ME into Sequential Model Editing (SME) to help develop more practical editing methods. Our study shows that most current ME methods could yield unsatisfying results in this scenario. We then introduce Transformer-Patcher, a novel model editor that can shift the behavior of transformer-based models by simply adding and training a few neurons in the last Feed-Forward Network layer. Experimental results on both classification and generation tasks show that Transformer-Patcher can successively correct up to thousands of errors (Reliability) and generalize to their equivalent inputs (Generality) while retaining the model's accuracy on irrelevant inputs (Locality). Our method outperforms previous fine-tuning and HyperNetwork-based methods and achieves state-of-the-art performance for Sequential Model Editing (SME). The code is available at https://***/ZeroYuHuang/Transform er-Patcher. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Network layers

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Improving COVID-19 Forecasts using Hybrid Machine Learning Models

Improving COVID-19 Forecasts using Hybrid Machine Learning M...

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Communications, Information, Electronic and Energy Systems (CIEES), International Conference on

作者： Michael Onuoha Aditya Kulkarni Navrati Saxena Dept. of Computer Science San Jose State University San Jose CA USA Software Engineer III Walmart Inc Redmond WA USA

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

The spread of COVID-19 has impacted human life in every aspect. Because of the uncertainty of the virus’s future impact, highly advanced machine learning approaches are needed to enhance the accuracy of COVID-19 forecasts. Therefore, we propose the following hybrid models to address the challenge: CNN-LSTM-Prophet, GRU-LSTM-Prophet, and RNN-LSTM-Prophet. The RNN-LSTM-Prophet model performs the best in predicting daily COVID-19 cases in the United States as it has the lowest MSE, RMSE, and MAE values. Moreover, all of the hybrid models perform better than the individual LSTM and Prophet models. Based on the benchmark results, RNN-LSTM-Prophet performs the best compared to the other models in this study. The hybrid model also performs better than the baseline as the MSE, RMSE, and MAE are lower by 2.48%, 1.24%, and 1.85% respectively. Governments worldwide can use RNN-LSTM-Prophet to better determine COVID-19’s future trajectory and then implement the appropriate policies and protocols. Moreover, the model can be used to deal with new diseases on the rise.

关键词： COVID-19 Accuracy Uncertainty Protocols computer viruses Government Machine learning Predictive models Trajectory Diseases

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International Joint Conference on Neural Networks (IJCNN)

作者： Guanming Chen Wenwen Qiang Yuanxin Ouyang Chuantao Yin Zhang Xiong Sino-French Engineer School Beihang University China Institute of Software Chinese Academy of Sciences China School of Computer Science and Engineering Beihang University China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Collaborative Filtering (CF) models based on Graph Contrastive Learning (GCL) have effectively improved the performance of long-tail recommendation. However, the popularity bias still presents a challenge in further enhancing their effectiveness. Some studies suggest that achieving better recommendations, particularly for the long-tail, requires learning representations with a more uniform distribution to implicitly mitigate popularity bias. Nevertheless, our analysis of various CF models reveals that different models exhibit varying abilities in capturing popularity and those with superior performance might encode more popularity information in item representations. This raises a question: Does encoding popularity always lead to harmful bias? We speculate that superior recommendations may emerge from leveraging the encoded popularity information to optimize the representations of users and items rather than eliminating its existence in representations. This motivates a data augmentation approach, wherein we generate augmented samples by mixing representations of items with different popularity levels and explicitly debias using the encoded popularity information which is often neglected. Additionally, we propose an adaptive contrastive loss, leveraging structural information and unifying the recommendation and contrastive learning objectives, which adaptively re-weights positive samples and ensures the capture of item popularity. Our proposed framework remains scalable without requiring multiple forward computations throughout the entire graph. Extensive experiments demonstrate improvements in both overall and long-tail recommendation performance.

关键词： Analytical models Collaborative filtering Scalability Computational modeling Noise reduction Neural networks Contrastive learning

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Two-Area Power System with Automatic Generation Control Utilizing PID Control, FOPID, Particle Swarm Optimization, and Genetic Algorithms 4

Two-Area Power System with Automatic Generation Control Util...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sati, Madan Mohan Kumar, Dinesh Singh, Akhilesh Raparthi, Mohan Alghayadh, Faisal Yousef Soni, Mukesh Graphic Era Hill University Deptt. of Mathematics Bhimtal263136 India Srm Institute of Science and Technology Dept. of Computer Science and Engg. UP Ghaziabad201204 India Uttarakhand Technical University Campus Institute Deptt. of Electrical Engg. Pithoragarh India Alphabet Life Science Software Engineer DallasTX75063 United States Almaarefa Univ Riyadh College of Applied Sci Computer Science and It Department Saudi Arabia Dr. D. Y. Patil Vidyapeeth School of Science and Technology Pune India

ISBN: (纸本)9798350343670

This study aims to present a feasible LFC (Load Frequency Control) model that integrates physical security limitations, namely GRC, GDB, and TD. The presence of this characteristic guarantees that the design is not only accurate but also capable of being materialized in a physical form. It is suggested that the traditional PI controller be replaced with a PID controller in order to handle the issues that are posed by large-scale power systems that are interconnected. Developing extremely efficient soft computing approaches that are capable of producing the necessary results for both individual and extensive power systems while taking into consideration flaws in modeling is the goal of this endeavor. It is important to get the frequency deviation to zero while still following the rules set by real issues and different working conditions. In order to maintain the electrical flow between the inter-area tie lines that are located within the various control zones at the schedule values that have been specified. In order to achieve optimal levels of performance in both steady state and transient conditions. The purpose of this is to obtain valuable multiple solutions with the intention of future planning and design. It is important to simplify the robust load frequency control design problem by changing it into a low-order proportional integral derivative (PID) control synthesis problem in order to address the practical benefits. This approach is commonly employed in real-world power systems applications. © 2024 IEEE.

关键词： and TD (Transmission Delay ANN BBBC FOPID GDB (Generation Demand Balance) GRC (Generation Rate Constraint) Load Frequency Control (LFC) PID PSO

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Enhancing Women's Safety Through Customized Smartphone Sensor Data: A Human Activity Recognition System 9

Enhancing Women's Safety Through Customized Smartphone Senso...

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9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Islam, Yamina Nowrin, Sanjida Faqih, Majed Ibrahim Sultana, Fozia Bahadur, Erfanul Hoque Masum, Abdul Kadar International Islamic University Chittagong Dept. of Computer Science and Engineering Chattogram Bangladesh Signal Corps Institute Director of Research and Development Saudi Arabia Hiperdyne Artificial Intelligence Engineer Tokyo Japan Daffodil International University Dept. of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798350319651

Human activity recognition (HAR) research using smartphone sensor technology has changed people's daily lives and opened the door for numerous exciting applications due to its extensive applications in addressing real-world humancentric issues. These studies, however, mostly concentrated on a woman's daily activities and behaviours when she is attacked, such as running, walking, sleeping, eating, drinking, lying, straight punching, front kicking, and so forth. The article offers a method to foresee human behaviour using a deep learning model and evaluates the efficiency of the approach using real-world data in response to this. We offer an LSTM structure for detecting the intensity of HAR in order to track activity patterns and build a neural network with binary classification skills to evaluate the situation of women. The proposed model uses the dataset to identify human activity, which records 30 volunteers' body movements while they engage in 18 physical activities. Feature engineering is used to extract additional variables from the data in addition to that. Our proposed model achieves a 90.89% accuracy. © 2023 IEEE.

关键词： Smartphones

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Mathura (MBI)-A novel imputation measure for imputation of missing values in medical datasets

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Recent Advances in computer science and Communications 2021年第5期14卷 1358-1369页

作者： Mathura Bai, B. Mangathayaru, N. Padmaja Rani, B. Aljawarneh, Shadi Department of Information Technology VNR VJIET Hyderabad India Department of Computer Science & Engineer-ing JNTUH Hyderabad India Department of Software Engineering Director of Information Technology and Com-munications Jordan University of Science and Technology Irbid Jordan

Aims: Propose an imputation measure for filling missing data values so as to make the incomplete medical datasets as complete datasets. Apply this imputation measure on imputed da-tasets to achieve improved classifier accuracies. Objective: The basic intention of the present study is to present an imputation measure to find the proximity between medical records and an approach for imputation of missing values in medical datasets to improve the accuracy of existing classifiers. Methods: The performance of proposed approach is compared to existing approaches with respect to classifier accuracy and also by performing non-parametric test called Wilcoxon test. Results & Conclusion: Experiments are conducted by considering three benchmark datasets CLEVALAND, PIMA, ECOLI and by applying proposed imputation technique with KNN, J48 and SMO classifiers and classifier accuracies are determined. The results obtained are then compared to thirteen existing benchmark imputation techniques available in KEEL repository. Experi-ment results proved the importance of the proposed imputation technique. © 2021 Bentham science Publishers.

关键词： Classification (of information)

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Smart Expiry Tracker and Product Recommender using Google ML Kit OCR Engine 2

Smart Expiry Tracker and Product Recommender using Google ML...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Krishanamurthy, Vallidevi Kurumthottam, Aleena Bosco Ali, Shireen Aliza Anandan, Paul Danny Kavitha, Muthu Subash Karthikeyan, K. Kumaresh, V. Manikandan, S. School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Mechanical Electrical and Manufacturing Engineering Loughborough University United Kingdom School of Information and Data Sciences Nagasaki University Japan Senior Data Analyst Ninjacart Bangalore India Software Development Engineer Vegrow Chennai India iOS App Developer Facilio Chennai India

ISBN: (纸本)9798331504106

Expiration dates play a crucial role in both personal and business contexts, influencing everything from health management to operational efficiency. Track Expiry Dates (TED) is a comprehensive mobile application designed to address two key needs: document management and expiry tracking. We designed a secure app which offers users to store personal documents such as driving licenses, passports, general documents, product warranty cards, and bills, ensuring that critical information is readily accessible and safeguarded against loss or damage. The proposed system provides a seamless and efficient solution for tracking expiry dates and storing essential documents such as Aadhar cards and PAN cards. It automatically sends reminders to users and their nominated family members prior to document expiration, ensuring timely renewals. From a business perspective, the platform enables companies to leverage data from uploaded documents to offer targeted, content-driven recommendations for products and brands. With its intuitive user interface and robust back-end infrastructure, the app delivers a comprehensive system for document management and expiry tracking, effectively meeting the diverse requirements of both individual users and businesses. ©2024 IEEE.

关键词： Driver licensing

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