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A Review on Optimal Deep learning Based Prediction Model for Multi Disease Prediction 1

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5th international conference on Smart Technologies in data Science and Communication, SMART-DSC 2022

作者： Minda, Aneel Kumar Ganesan, Vithya International SOS Dubai United Arab Emirates CSE Koneru Lakshmiah Education Foundation Guntur India

ISBN: (数字)9789811968808

ISBN: (纸本)9789811968792

Healthcare data collecting and processing is one of the most worrisome troublesome to optimize the methodology. With the advent of the digital era and technological advancements, a vast quantity of multidimensional data on patients is created, including clinical factors, hospital resources, illness diagnostic information, patients’ records, and medical equipment. the enormous, dense, and complex data must be processed and evaluated to extract knowledge for effective decision-making. Medical data mining offers a lot of potential for uncovering hidden patterns in medical data sets. By identifying significant patterns and detecting correlations and relationships among many variables in huge databases, the use of various data mining tools and machine learning approaches has changed healthcare organizations. this review paper identifies an importation in the medical data, providing and comparing existing data for the future course of action. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data science for healthcare Disease predication Health care Health prediction Medical care data analysis

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Utilization of machine learning Algorithms to Monitor the Growth Path of Fishes in Marine Aquaculture 15th

Utilization of Machine Learning Algorithms to Monitor the Gr...

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15th international conference on Soft Computing and pattern recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Ajibade, Samuel-Soma M. Essah, Richard Tapales, Catherine P. Antecristo, Charmaine P. Cababat, Feliciana G. Oyebode, Oluwadare Joshua Akintoye, Kayode A. Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Malaysia Department of Computer and Information Sciences Sunway University Selangor Malaysia Department of Computer Science and Engineering Chandigarh University Punjab India College of Arts and Sciences Cebu Technological University Cebu Moalboal Philippines Cebu Technological University Cebu Moalboal Philippines College of Education Cebu Technological University Cebu Moalboal Philippines Department of Civil and Environmental Engineering ABUAD Ado Ekiti Nigeria Department of Computer Science The Federal Polytechnic Ekiti State Ado Ekiti Nigeria

ISBN: (纸本)9783031810855

Under a realistic cellular metabolic economic model of Nile tilapia, this work investigates Q-learning fish growth trajectory analysis. we offer two Q-learning data mining algorithms the best management policy using simulated data of optimum growth projections starting at the early growth stage and ending at appropriate market share. the early or primitive Q-learning technique learns optimum feed supplying regulatory regime for fish cultivation rate for the fished present the cages, whereas other one continuously adjusts appropriate nutrition controls the law for agricultural fish rate of increase in tanks together within ideal temperature pattern. the cumulative percentage weight of fishes through both vessels on land and afloat cages was tracked with relative dynamic model errors of 1.7% and 6.6%, respectively, using Q-learning control rules. Furthermore, contrasted to drifting cages where its water temp is controlled at 29.7 ℃, the eating and heat management policies minimise the proportional rate of feeding along with wastage of food in by 11%. data, algorithms, and performance of DL approaches used in adaptive aquaculture farms are also examined. In a nutshell, our goal is to give scholars and clinicians a greater grasp of the latest advancements of deep learning in aquaculture, which will help them develop smart aquacultural applications. With improved machine - learning technologies, a video trying to track biological early warning has made significant progress. Artificial intelligence has helped researchers better understand behavioural reactions to pharmacological and environmental stress. We discuss pioneer efforts in exact monitoring of a gathering of individuals in 2d/3d space in this paper, which introduces the fundamentals of activity recognition. Video tracking's professional and operational difficulties are discussed. the toxicological study based on fish behavioral data is then summarized. © the Author(s), under exclusive license to Springer Nature

关键词： Deep learning

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A New Business Proposal for Attainment of the Sustainable Development Goals Based on data mining 6

A New Business Proposal for Attainment of the Sustainable De...

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6th international conference on Artificial Intelligence and Big data, ICAIBD 2023

作者： Xia, Runpeng Yamaguchi, Mae Matsumoto, Miyuki Hayashi, Hiromichi Ogawa, Reo The University of Tokyo Systems Innovation Tokyo Japan

ISBN: (纸本)9781665491259

the sustainable development goals (SDGs) are international goals aimed at achieving a sustainable and better world by 2030. As the importance of the SDGs grows these years, more and more people are becoming aware of them. However, compared to the high awareness, progress towards the SDGs in Japan is still low due to many people not putting the SDGs into practice. To address this issue, this paper adopts data from ADK General Consumer Survey 2020 and conducts analysis on it based on data mining techniques. We first conduct factor analysis on the consumption tendency of people who show low affirmations towards the SDGs, then construct a prediction model to predict people's affirmations towards the SDGs using machine learning methods. Based on these endeavors, we propose a new business aiming to encourage people to buy more sustainable products and contribute to the society's attainment of the SDGs. this paper also devises the mobile app for our proposed business and illustrates its application in reality. Although our new business mainly targets the Japanese market, it can also be introduced to other regions to promote the realization of a sustainable society. © 2023 IEEE.

关键词： data mining

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Research on the Application of Artificial Intelligence in Cybersecurity: Integrating Advanced Technologies to Improve threat Detection and Response 5

Research on the Application of Artificial Intelligence in Cy...

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5th IEEE Global conference for Advancement in Technology, GCAT 2024

作者： Balaji, T.S. Nadanasabai, R. Ravikumar, Rajesh Jagadeesan Sharanyaa, S. Meenal, R. Deepthi, Nandigam Poornima Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Vadapalani Campus Chennai India St.Josephs College of Engineering Department of Mba Chennai India Evernorth Health Services ChattanoogaTN37402 United States Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India V.S.B Engineering College Department of Electrical and Electronics Engineering Karur India Mlr Institute of Technology Department of Ece Dundigal Hyderabad India

ISBN: (纸本)9798350376685

In the current age of digital change, the field of cybersecurity is faced with progressively advanced and enduring dangers. Conventional security measures are finding it difficult to keep up with the fast-changing nature of cyber threats. Artificial intelligence (AI) offers a significant chance to enhance threat detection and response systems by leveraging its sophisticated data processing and pattern recognition abilities. this study explores the incorporation of artificial intelligence (AI) technology in the field of cybersecurity, specifically aiming to improve the effectiveness of systems that identify and respond to threats. the paper commences by examining the present difficulties encountered by cybersecurity experts, which encompass the large number and diverse range of potential threat vectors, the covert and flexible nature of contemporary malware, and the limitations imposed by resource constraints on the efficacy of human-driven analysis. the text explores the current cutting-edge AI strategies used to tackle these difficulties, including anomaly identification through machine learning algorithms, predictive analysis using deep learning models, and threat intelligence collecting through natural language processing. the research also examines the distinct factors of AI in the cybersecurity field, such as the requirement for immediate decision-making, the significance of comprehending the model, and the possibility of hostile attacks on AI systems. this study seeks to analyse current literature and emerging trends to gain a deep understanding of how artificial intelligence (AI) can enhance cybersecurity. Additionally, it aims to identify the obstacles and deficiencies that must be overcome to effectively incorporate AI technologies into this crucial domain. To summarise, the research highlights the considerable capacity of AI to transform cybersecurity practices, providing a way to develop more robust and flexible defence methods against the numerous danger

关键词： Adversarial machine learning

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Leveraging DQN-Based Recommender Systems for E-Commerce in Smart Cities 4th

Leveraging DQN-Based Recommender Systems for E-Commerce in S...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Abakarim, Sana Qassimi, Sara Rakrak, Said Cadi Ayyad Univ Fac Sci & Tech L2IS Lab Marrakech Morocco

ISBN: (纸本)9783031821523;9783031821530

In the age of digital urban transformation, smart cities are emerging as ecosystems where technology, infrastructure and data converge to improve quality of life, sustainability and economic prosperity. In this context, e-commerce plays a central role, providing platforms for businesses to thrive by enabling seamless transactions, personalised shopping experiences and greater market reach. However, the dynamic and evolving nature of user preferences presents a significant challenge, requiring more adaptive and smart recommendation systems. this paper presents an approach by integrating Deep Q-Network (DQN), a reinforcement learning technique, into recommendation systems for e-commerce in smart cities. By comparing the proposed DQN model based recommender system with traditional models such as MLP, DeepFM, LSTM and CNN using metrics such as MSE, RMSE and NDCG@5, we demonstrate its superior performance in predicting user preferences and dynamically adapting to changes in user behaviour. the results highlight the potential of DQN models to revolutionise e-commerce recommender systems, delivering more personalised and adaptive user experiences in the interconnected environments of smart cities.

关键词： Deep Q-Network Recommender Systems Reinforcement learning Smart Cities E-Commerce machine learning Deep Reinforcement learning

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Indicators of Compromise Automatic Identification Model Based on Cyberthreat Intelligence and Deep learning 5

Indicators of Compromise Automatic Identification Model Base...

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5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Tang, BinHui Qiu, Huanran Sichuan University Chengdu Jincheng College School of Cyber Science and Engineering Chengdu China Chengdu Jincheng College Chengdu China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

In recent years, cyber attacks have become more serious, and traditional defense methods can no longer cope with the ever-changing ways of cyber attacks, leading to severe social losses. Identifying malicious attack indicators quickly and accurately is crucial to cybersecurity. therefore, we propose an automatic threat indicator identification model based on a deep learning model that can automatically identify cyber threat indicators from cyber threat intelligence. Since the traditional machine learning approach is limited by feature engineering, deep learning-based methods become an effective solution. Firstly, web crawler technology obtains web threat intelligence data from security forums, blogs, and other platforms. then, the text data are preprocessed and input to a word embedding model to extract feature vectors. Finally, the classification model is trained on the collected dataset using TextRCNN to automatically recognize statements containing threat IOCs in web threat intelligence. the experimental results show that the method analyzes the local semantics using the convolutional layer while combining with the cyclic layer to explore the contextual information of the statements, which eventually be effectively used for the classification of IOC statements and accurately identify the threat text statements containing IOCs, with an accuracy rate of 95.86% through the model and improves the efficiency and accuracy of security text analysis. © 2022 IEEE.

关键词： Network security

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Heart Disease Prediction Using machine learning Algorithm 5

Heart Disease Prediction Using Machine Learning Algorithm

引用

5th international conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Sasipriya, S. Bala Nimisha, K. Anusha, R. Arun Pandiyan, G. Sri Krishna College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350321425

Health-care organizations collect huge amounts of data, which might include certain hidden facts that could be utilized to make better decisions. Some sophisticated data mining methods are utilized to provide suitable findings and make good data judgements. In this study, affinity propagation clustering & neural networks are utilized to build an adequate heart disease predicting system for anticipating the risk level of heart disease. the algorithm predicts using 15 medical criteria such as age, gender, blood pressure, diabetes, and obesity. the EHDPS predicts a patient's risk of developing heart disease. It enables the generation of a large amount of data, such as associations between medical indicators related to heart disease as well as patterns. A multilayer neural network containing back propagation has been employed as the training approach. Clustering has been performed using affinity propagation. ANN algorithm has been used. Certain parameters such as accuracy, sensitivity, specificity, Recall and F-score has been calculated. the results demonstrate that the proposed diagnostic approach may properly predict the risk level of heart diseases. © 2023 IEEE.

关键词： Health care

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Digitization of Written Parliamentary Questions from the Historical Archive (1974-1977) of the Hellenic Parliament

Digitization of Written Parliamentary Questions from the His...

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18th international conference on Document Analysis and recognition (ICDAR)

作者： Fitsilis, Fotios Gatos, Basilis Palaiologos, Konstantinos Kaddas, Panagiotis Kyrkos, Charalambis Georgoulea, Maria-Eleni Armenakis, Yiannis Tasouli, Christina Mikros, George Rozenberg, Olivier Kiousi, Eleni Hellen Parliament Vas Sofias Ave 2-4 Athens 10021 Greece Natl Ctr Sci Res Demokritos Patriarchou Grigoriou E & Neapoleos 27 Aghia Paraskevi 15341 Greece Hellen OCR Team Aretis 5B Aghia Paraskevi 15343 Greece Hamad Bin Khalifa Univ Educ City Gate 8 Ar Rayyan Qatar

ISBN: (纸本)9783031706448;9783031706455

this article outlines the digitization process and methodology applied to the archive of parliamentary questions from the 1st Parliamentary Term (1974-1977) in the Hellenic Parliament. A collaborative pilot project involving parliament, academia, and a research center facilitated the conversion of printed material to open data. the main tasks of the project include capturing digital images, a custom Optical Character recognition (OCR) software solution employing machine learning, and rigorous validation for accuracy of a fragmented and of variable quality polytonic corpus in a variety of modern Greek language called Katharevousa. the article discusses the approach and challenges as well as the initial results of the digitization effort, emphasizing ongoing research steps. Overall, 1,674 images were digitally processed corresponding to 1,338 questions. Following algorithmic training, character recognition accuracy is over 98.5%. Successful implementation streamlines further similar digitalization operations in the vast parliamentary archives, while enabling in-depth studies on parliamentary control in the turbulent period of the immediate post-junta era in Greece. A preliminary comparative analysis with a corpus of newer parliamentary questions (2009-2019) provides insights and incentives for the further study of the characteristics and evolution of the Greek language.

关键词： Hellenic Parliament written questions machine learning OCR parliamentary control polytonic corpus

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QuantFace: Towards Lightweight Face recognition by Synthetic data Low-bit Quantization 26

QuantFace: Towards Lightweight Face Recognition by Synthetic...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Boutros, Fadi Damer, Naser Kuijper, Arjan Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Deep learning-based face recognition models follow the common trend in deep neural networks by utilizing full-precision floating-point networks with high computational costs. Deploying such networks in use-cases constrained by computational requirements is often infeasible due to the large memory required by the full-precision model. Previous compact face recognition approaches proposed to design special compact architectures and train them from scratch using real training data, which may not be available in a real-world scenario due to privacy concerns. We present in this work the QuantFace solution based on low-bit precision format model quantization. QuantFace reduces the required computational cost of the existing face recognition models without the need for designing a particular architecture or accessing real training data. QuantFace introduces privacy-friendly synthetic face data to the quantization process to mitigate potential privacy concerns and issues related to the accessibility to real training data. through extensive evaluation experiments on seven benchmarks and four network architectures, we demonstrate that QuantFace can successfully reduce the model size up to 5x while maintaining, to a large degree, the verification performance of the full-precision model without accessing real training datasets. All training codes are publicly available(1).

关键词： Training data privacy Quantization (signal) Computational modeling Face recognition Neural networks Training data

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Online Transaction Anomaly Detection Model for Credit Card Usage Using machine learning Classifiers 5

Online Transaction Anomaly Detection Model for Credit Card U...

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5th international conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Jayasingh, Bipin Bihari Sri, G. Bhagya Cvr College of Engineering It Department Hyderabad India

ISBN: (纸本)9781665475242

the methods for payment of an online transaction are credit or debit card, online bank transfer, e-wallets, mobile payments, etc. the process of electronic fund transfer is secure, and password protected, as claimed by the vendors. the security threats for all payment methods exist with an intention, like credit and debit card frauds. Monitoring fraudulent activities becomes more important as the number of online transactions for credit card usage grows over time. the detection of deviations from the large number of transactions in cases of frauds in credit card is desired using the classifiers of machine learning. We proposed to develop a Transaction Anomaly Detection (TAD) model for online transactions during credit card usage by customers using machine learning. the model is built to exploit and expose fraudulent transactions during online transactions at e-commerce sites. this work considers a data set from *** that has 28,4807 records of credit card transactions online with a class label. the proposed TAD model applies various machine learning algorithms to calculate the performance metrics and finds an efficient algorithm for detecting online transaction anomalies with good accuracy and recall. We observed that the XGB Classifier classifies the fraudulent transactions with an accuracy of 99.96% and a recall of 83%, which is the best suitable algorithm for this dataset. © 2023 IEEE.

关键词： data mining

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