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Hybrid AI: The Way Forward in AI by Developing Four Dimensions 1

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1st international workshop on Trustworthy AI – Integrating learning, Optimization and Reasoning, TAILOR 2020 held as a part of European Conference on Artificial Intelligence, ECAI 2020

作者： Huizing, Albert Veenman, Cor Neerincx, Mark Dijk, Judith TNO The Hague Netherlands LIACS Leiden University Leiden Netherlands Delft University of Technology Delft Netherlands

ISBN: (数字)9783030739591

ISBN: (纸本)9783030739584

In recent years, AI based on deep learning has achieved tremendous success in specialized tasks such as speech recognition, machine translation, and the detection of tumors in medical images. Despite these successes there are also some clear signs of the limitations of the current state-of-the-art in AI. For example, biases in AI-enabled face recognition and predictive policing have shown that prejudice in AI systems is a real problem that must be solved. In this position paper, we argue that current AI needs to be developed along four dimensions to become more generally applicable and trustworthy: environment, purpose, collaboration, and governance. Hybrid AI offers the potential for advancements along these four dimensions by combining two different paradigms in AI: knowledge-based reasoning and optimization, and data-driven machine learning. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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Deep learning assisted heart arrhythmia detection 1

Deep learning assisted heart arrhythmia detection

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1st international Conference on Computational Intelligence and Energy Advancements, ICCIEA 2020

作者： Fernandes, Johnathan Chudgar, Sarthak Dharap, Harshal Poduval, Aneesh Department of Instrumentation and Control Engineering Vishwakarma Institute of Technology Pune Maharashtra India

Heart arrhythmia, or irregular heart rhythm, is an extremely common heart affliction experienced by a large percent of the world's population every day, mostly going unnoticed. However, if left unchecked for an extended period of time, it poses an inherent risk to human life. Advancements in technology have enabled us to leverage the awesome computational power of graphics processing units in parallel in order to derive solutions to real life medical issues, by analyzing tremendous amounts of data in a relatively short amount of time. Owing to their ability to parse huge amounts of data and quickly perform multiple complex computations in parallel, machine learning algorithms have repeatedly and consistently outperformed humans in tasks such as pattern recognition and data analysis. Through this research project, we seek to contribute to the medical field by implementing deep learning technology along with machine learning algorithms into a system which can detect heart arrhythmias from electrocardiogram (ECG) reports quickly and effectively. © Published under licence by IOP Publishing Ltd.

关键词： Heart

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Designing intelligent systems for online education: Open challenges and future directions 1

Designing intelligent systems for online education: Open cha...

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1st international workshop on Enabling data-Driven Decisions from learning on the Web, L2D 2021

作者： Dessì, Danilo Käser, Tanja Marras, Mirko Popescu, Elvira Sack, Harald Epfl Switzerland Fiz Karlsruhe - Leibniz Institute for Information Infrastructure Germany Karlsruhe Institute of Technology Institute Aifb Germany University of Craiova Romania

The design and delivering of platforms for online education is fostering increasingly intense research. Scaling up education online brings new emerging needs related with hardly manageable classes, overwhelming content alternatives, and academic dishonesty while interacting remotely, as examples. However, with the impressive progress of the data mining and machine learning fields, combined with the large amounts of learning-related data and high-performance computing, it has been possible to gain a deeper understanding of the nature of learning and teaching online. Methods at the analytical and algorithmic levels are constantly being developed and hybrid approaches are receiving an increasing attention. Recent methods are analyzing not only the online traces left by students a posteriori, but also the extent to which this data can be turned into actionable insights and models, to support the above needs in a computationally efficient, adaptive and timely way. In this paper, we present relevant open challenges lying at the intersection between the machine learning and educational communities, that need to be addressed to further develop the field of intelligent systems for online education. Several areas of research in this field are identified, such as data availability and sharing, time-wise and multi-modal data modelling, generalizability, fairness, explainability, interpretability, privacy, and ethics behind models delivered for supporting education. Practical challenges and recommendations for possible research directions are provided for each of them, paving the way for future advances in this field. © 2021 CEUR-WS. All rights reserved.

关键词： E-learning

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Effective Intrusion Detection and Classification using Fuzzy Rule based Classifier in Cloud Environment 1

Effective Intrusion Detection and Classification using Fuzzy...

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1st IEEE international Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Veena, C. Ramalakshmi, S. Bhoopathy, V. Bhosale, Minakshi Dattatraya Magadum, C.G. Abirami, S.K. Ghatkesar Telangana Hyderabad India Puducherry Karaikal India Department of Computer Science and Engineering Malla Reddy College of Engineering Telangana Secunderabad India Faculty of MCA Yashoda Technical Campus Maharashtra Satara India Smt. Kasturbai Walchand College Maharashtra Sangli India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9781665460842

Cloud computing (CC) is the demonstration of the technology that makes use of the substructure for computing in a proficient fashion. This sort of computing offers great quantity of consequences in augmenting the productivity that verifies the risk handling management and decreases the cost. Intrusion detection system (IDS) is widely applied for detecting malicious actions in its host and the communication network. IDS is a procedure of discovering activities that take place in a network and attempts to fulfill the confidentiality, network, or security accessibility to smear the trust procedure. This article introduces an Effective Intrusion Detection and Classification using Fuzzy Rule based Classifier (EIDC-FRC) model in Cloud Environment. The goal of the presented EIDC-FRC model is to determine the occurrence of intrusions and normal data traffic in the cloud environment. In order to achieve this, the presented EIDC-FRC model applies FRC for data classification process. The parameter tuning of the FRC model is performed using enhanced bird swarm algorithm (EBSA). The FRC is an effective model in pattern recognition that offers effective outcomes by the use of linguistic labels in the antecedents of the rules. The simulation analysis of the EIDC-FRC model ensured the enhancements of the presented approach compared to recent state of art approaches. © 2022 IEEE

关键词： machine learning

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Accidental Face recognition and Detection Using machine learning

Accidental Face Recognition and Detection Using Machine Lear...

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Advanced Innovations in Smart Cities (ICAISC), international Conference on

作者： Ashish Sharma Piyansh Agrawal Krishan Bhisham Sharma Imed Ben Dhaou Department of Computer Engineering & Applications GLA University Mathura UP India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Hekma School of Engineering Computing and Informatics Dar Al-Hekma University Jeddah Saudi Arabia

The data collected from all the states and Union Territories has been compiled in the Publication, according to the ministry of road transport and roads transport research wing. The total number of accident-related deaths in 2018 was 1,51,417, which is a 2.3 percent increase over 2017. Around 85% of accident-related deaths occur in the 18-60 age range, which is the most productive. Road traffic fatalities not only inflict the relatives of the victims considerable emotional suffering, but they also cost the nation a lot of money. In this data maximum hazard happens due to delayed response of family and friends as they are unknown of the situation of the sight of the accident. Also, several cases remain unreported. Our objective is to reduce this number to make our nation strong and prosper. In this current research we are committed to creating a social network where road accidents can be reported quickly to family and friends, so the delayed response can be reduced. The practice of associating a person with a picture has become increasingly common thanks to the media. However, it is less resistant to retinal and fingerprint scanning. The face detection and recognition module created for the current research is described in this paper. Face detection will be performed using Haar-Cascades, while face identification will be performed using Eigenfaces, Fisher faces, and local binary pattern histograms.

关键词： Resistance Technological innovation Road accidents Social networking (online) Smart cities Face recognition Roads

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Field-effect-transistor based biosensors: a review of their use in environmental monitoring applications 3

Field-effect-transistor based biosensors: a review of their ...

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1st IEEE international workshop on Metrology for the Agriculture and Forestry (IEEE MetroAgriFor)

作者： Elli, Giulia Ciocca, Manuela Lugli, Paolo Petti, Luisa Free Univ Bolzano Bozen Fac Sci & Technol I-39100 Bolzano Italy

ISBN: (纸本)9781665405331

Biosensors are analytical devices used to detect the presence of a specific bio-chemical substance (called analyte), through the combination of a biorecognition element with a physico-chemical transducer. Biosensors using field-effect transistor (FET) transducing platforms (called Bio-FETs) exploit the variations of the current between a source and a drain terminal upon specific (physical or chemical) interactions in presence of the analyte of interest. While Bio-FETs have mainly been used in various fields of application such as medical diagnostics, biomolecule detection (like glucose) and food safety, environmental monitoring is still a quite unexplored area. In particular for agricultural applications, it is extremely important to develop real-time, portable, and low-cost monitoring devices for various types of pesticides, especially considering that nowadays many of these are considered environmental contaminants and are being banned. Only recently some studies have reported the development of Bio-FETs for the detection of a particular type of pesticides: herbicides. In this review, we revise the use of Bio-FETs for environmental monitoring applications. First we highlight the working principles of Bio-FETs, presenting the most commonly used material and the possible device configurations. Then we present the most relevant studies employing Bio-FETs for environmental monitoring. Finally, we show a quite new approach for Bio-FET, which is the coupling of biosensors with artificial intelligence and machine learning algorithms, to analyse complex data.

关键词： Couplings Transducers machine learning algorithms Target recognition Metrology Real-time systems Safety

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1st international Conference on Applied Intelligence and Informatics, AII 2021

1st International Conference on Applied Intelligence and Inf...

引用

1st international Conference on Applied Intelligence and Informatics, AII 2021

ISBN: (纸本)9783030822682

The proceedings contain 30 papers. The special focus in this conference is on Applied Intelligence and Informatics. The topics include: Key Techniques and Challenges for Processing of Heart Sound Signals;enhanced Signal Processing Using Modified Cyclic Shift Tree Denoising;a machine learning Driven Android Based Mobile Application for Flower Identification;a Generative Text Summarization Model Based on Document structure Neural Network;human Gender Detection from Facial Images Using Convolution Neural Network;few-Shot learning for Tamil Handwritten Character recognition Using Deep Siamese Convolutional Neural Network;A CNN Based Model for Venomous and Non-venomous Snake Classification;recognition of Dysfluency in Speech: A Bidirectional Long-Short Term Memory Based Approach;distributed Denial of Service Attack Detection Using machine learning and Class Oversampling;glaucoma Detection Using Inception Convolutional Neural Network V3;scientific Metrological Analysis of Government Services Based on Big data Analysis and Visualization Software Driven by Information Technology;violent Video Event Detection: A Local Optimal Oriented pattern Based Approach;human Age Estimation Using Deep learning from Gait data;an Error Resilient Video Transmission in Ad Hoc Network Using Error Diffusion Block Truncation Coding;ALO: AI for Least Observed People;COVID-Hero: machine learning Based COVID-19 Awareness Enhancement Mobile Game for Children;Literature Classification Model of Deep learning Based on BERT-BiLstM——Taking COVID-19 as an Example;identifying Relevant stakeholders in Digital Healthcare;COVID-19 Detection Using Chest X-Ray Images with a RegNet structured Deep learning Model;mixed Bangla-English Spoken Digit Classification Using Convolutional Neural Network;iConDet: An Intelligent Portable Healthcare App for the Detection of Conjunctivitis.

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An Efficient pattern recognition Based Method for Drug-Drug Interaction Diagnosis 1

An Efficient Pattern Recognition Based Method for Drug-Drug ...

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1st international Conference on Artificial Intelligence and data Analytics, CAIDA 2021

作者： Javed, Rabia Saba, Tanzila Humdullah, Salman Mohd Jamail, Nor Shahida Javed Awan, Mazhar Lahore College for Women University Computer Science Department Lahore54300 Pakistan Prince Sultan University Computer and Information Science Riyadh11586 Saudi Arabia Universiti Teknologi Malaysia Faculty of Computing Johor Bahru81310 Malaysia University of Management and Technology Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9780738131771

The diagnosis of interactions between two drugs is an essential procedure in drug development. Many medical tool's offer inclusive records related to DDI. However, this tool's results are not very satisfactory. The main aim is to propose an efficient approach based on pattern matching that identifies the interaction between two drugs. In this study, the goal is to collect the data from the DrugBank, which is a publicly available source. The drug-related data includes drug ID, drug names, and various kinds of sentences of drug-drug interaction. Drug names will be identified by drug names dictionary defined in the corpus, and sentences will be determined according to given patterns. These sentences will treat as input data, and preprocessing steps will perform in these sentences. Various types of features are selected for machine learning classification. Then all the attributes will be classified into desired classes. The proposed method gains 95.4% accuracy from the random forest classifier. © 2021 IEEE.

关键词： Diagnosis

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Violent Video Event Detection: A Local Optimal Oriented pattern Based Approach 1st

Violent Video Event Detection: A Local Optimal Oriented Patt...

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1st international Conference on Applied Intelligence and Informatics (AII)

作者： Lohithashva, B. H. Aradhya, V. N. Manjunath JSS Sci & Technol Univ Dept Comp Applicat Mysuru Karnataka India

ISBN: (纸本)9783030822699;9783030822682

Video/Camera-based monitoring is a prominent and difficult research problem in the field of machine learning and pattern recognition and posed much interest in our safety in the private and public sectors. Therefore, surveillance cameras have been deploying to control suspicious activity. Consequently, many researchers have worked on developing an automatic surveillance system to detect violent events and assists security guards to take the right decision at the right time. still, violent event detection is difficult to detect because of illumination, complex background, scale variation, blurriness, occlusion, and low resolution in a surveillance camera. In this paper, the Local Optimal Oriented pattern (LOOP) texture-based feature descriptor is proposed. Eventually, eminent features are used with a support vector machine (SVM) classier for violent event detection. Experiments are conducted on the Hockey Fight dataset and Violent-Flows dataset. The five-Fold Cross-Validation approach is used to analyze the performance of the proposed method. The data and results are promising and encouraging.

关键词： Violent event Texture features Spatio-temporal LOOP descriptor

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Generative Adversarial Network-a Comparative Analysis

Generative Adversarial Network-a Comparative Analysis

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international Conference on Computational Science and Technology (ICCst)

作者： S. Gurusubramani B. Latha Dept. of CSE Sri Sai Ram Engineering College

ISBN: (纸本)9781665476560

machine learning (ML) is a subset of artificial intelligence domain that incorporates a wide range of techniques including Genetic algorithms, Artificial Neural Networks, Bayesian learning, decision trees. Analytical learning and other supervised or unsupervised learning models. These techniques exploit present and arriving data to recognize the patterns and foresee forthcoming clinical events. There are number of evolving applications of ML in other aspects of patient management that have not yet been analyzed. Generative Adversarial networks (GANs), a deep neural networks which are capable of generating or transforming images and can support in faster imaging by creatingrealistic images utilizing the current imaging protocols in a variety of contrast and modalities. This paper deals with the big picture analysis of GAN along with architecture, comparison with existing approaches, application areas and the challenges associated with it.

关键词： Deep learning Analytical models Protocols Scientific computing learning (artificial intelligence) Generative adversarial networks pattern recognition

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