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Design Guide Prediction Model for the Legal Interactions (GPM-LI) 1

引用

20th International Conference on Hybrid intelligent systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired computing, NaBIC 2020

作者： Kadhum, Ghydaa Al-Janabi, Samaher University of Babylon Babylon Iraq

ISBN: (数字)9783030730505

ISBN: (纸本)9783030730499

There is a lot of time and money wasted during incorrect interactions in many laboratories in different fields (chemical, physical, medical, biology). The process of finding a correct interaction before entering laboratories of various kinds is one of the most important challenges in the field of scientific research as a result of the progress achieved in the field of intelligent computing. Therefore, in this paper, we will seek to solve the above challenge by building an integrated software model to test the interactions and ensure their accuracy before they are performed in the laboratories. This paper introduces the GPM-LI model for predicting optimal interactions. GPM-LI consists of several stages, in first stage separates the group of interaction into their primary elements, then the model selects the roots to build the set of possible interactions, where the root is not chosen randomly, but rather specifically through using (DSelect), after that the best set of interactions is found (finding the set of optimal path of interaction) through the use of our new algorithm Legal Tracking Interaction by Whale Optimization (LTI-WO), this algorithm find An optimal set of possible interactions, and the optimal interaction with GPM-LI represents the least expensive and time-consuming interaction, and the last stage is the evaluation of results through the use of the conflict matrix. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Software testing

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Sparse Wavelet Auto-encoder for Covid-19 Cases Identification 1

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20th International Conference on Hybrid intelligent systems, HIS 2020 and 12th World Congress on Nature and Biologically Inspired computing, NaBIC 2020

作者： Lazrag, Houda Ali, Ramzi Ben Ejbali, Ridha Research Team in Intelligent Machines National Engineering School of Gabes Gabes6072 Tunisia

ISBN: (数字)9783030730505

ISBN: (纸本)9783030730499

Diagnosis and understanding of disease progression require an interpretation of medical images, would take a lot of time in manual interpretation of the large amount of medical images accumulated. Thus, the automatic analysis and understanding of the medical images becomes an active research topic. In this case, feature extraction from the medical images plays an important role in obtaining diagnostic performance. In this context, we propose a Covid-19 cases identification based on sparse coding, wavelet analysis for feature extraction and AE for feature modeling. Our approach is based on sparse coding and wavelet analysis techniques for image representation and it is tested with the COVID-19 dataset. The experimental results demonstrate the performance of our system. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature extraction

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Learning Vector Quantization and Radial Basis Function Performance Comparison Based Intrusion Detection System

Learning Vector Quantization and Radial Basis Function Perfo...

引用

4th International Conference on intelligent Human systems Integration (IHSI) - Integrating People and intelligent systems

作者： Hounsou, Joel T. Niyomukiza, Pamela Belise Ciza Nsabimana, Thierry Vlavonou, Ghislain Frati, Fulvio Damiani, Ernesto Inst Math & Sci Phys Dangbo Benin Ecole Super Genie Telecoms Dangbo Benin TIC ECCE HOMO Dangbo Benin Univ Milan Comp Sci Dept Milan Italy

ISBN: (纸本)9783030680176;9783030680169

Information system's technologies increase rapidly and continuously due to the huge traffic and volume of data. Stored data need to be secured adequately and transferred safely through the computer network. Therefore the data transaction mechanism still exposed to the intrusion attack of which consequences remain unlikable. An intrusion can be understood as a set of actions that can compromise the three security purposes known as Confidentiality, Integrity and Availability (CIA) of resources and services. In order to face on these intrusions, an efficient and robust Intrusion Detection System (IDS) which can detect successfully the intrusion is strongly recommended. An IDS is a network/host security tool used for preventing and detecting malicious attacks which could make a system useless. The purpose of this paper is to implement network intrusion detection system based on machine learning using Artificial Neural Network algorithms specifically the Learning Quantization Vector and Radial Basis Function make the comparison on the performance between these two algorithms.

关键词： Intrusion Detection System Artificial Neural Network Learning Vector Quantization Radial Basis Function

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Capacity Enhancement of High Throughput Low Earth Orbit Satellites in a Constellation (HTS-LEO) in a 5G Network 1

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Future of Information and Communication Conference, FICC 2021

作者： Siddiqui, Arooj Mubashara Evans, Barry Zhang, Yingnan Xiao, Pei Home of 5G Innovation Centre Institute for Communication Systems University of Surrey Guildford SurreyGU2 7XH United Kingdom CAST Xi’an China

ISBN: (数字)9783030731007

ISBN: (纸本)9783030730994

The global telecommunication market aims to fulfil future ubiquitous coverage and rate requirements by integrating terrestrial communications with multiple spot beam high throughput satellites (HTS). In this paper a new scheme is proposed to connect multiple Low Earth Orbit (LEO) satellites in a constellation to a single gateway to support integrated-satellite-terrestrial networks. A single gateway with multiple steerable antenna arrays is proposed for reduction in gateway numbers and cost. Using a power allocation strategy, the target is to maximize the gateway link capacity of the HTS-LEO satellites for operation including feasible used cases studies of 3GPP for necessary adaptations in a 5G system. Firstly, an objective function is established to find the optimal power levels required. Secondly the interference from neighbouring satellite beams is considered to achieve maximum capacity. Mathematical formulations are developed for this non-convex problem. Simulation results show that the proposed system architecture improves capacity and meets the dynamic demand better than traditional methods. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： 5G mobile communication systems

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Prediction and Classification of Biased and Fake News Using NLP and Machine Learning Models 1

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2nd International Conference on Machine Learning and Information Processing, ICMLIP 2020

作者： Ghadekar, Premanand Tilokchandani, Mohit Jevrani, Anuj Dumpala, Sanjana Dass, Sanchit Shinde, Nikhil Department of Information Technology Engineering Vishwakarma Institute of Technology Pune India

ISBN: (数字)9789813348592

ISBN: (纸本)9789813348585

Fake news and Bias news play a very important role in spreading misinformation and thereby manipulating people’s perceptions to distort their awareness and decision-making. In the proposed model for finding the bias, sentiment analysis has been used and for fake news classification, the passive-aggressive classifier has been used. The accuracy achieved for fake news classification using passive aggressive is 95.9%. The bias of an article is calculated based on the sentiment score and the score assigned to the author and publisher based on their average bias score. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Design of a Semi-automatic Machine for Processing Ecological and Antibacterial Bricks to Save Concrete Based on Polyethylene and Copper Terephthalate Residues in Huancayo

Design of a Semi-automatic Machine for Processing Ecological...

引用

4th International Conference on intelligent Human systems Integration (IHSI) - Integrating People and intelligent systems

作者： Ibarra, Antony Quispe, Grimaldo Raymundo, Carlos Univ Continental Fac Ingn Huancayo Peru Univ Nacl Autonoma Altoandina Tarma Fac Ingn Tarma Peru Univ Peruana Ciencias Aplicadas Direcc Invest Lima 15023 Peru

ISBN: (纸本)9783030680176;9783030680169

The brick-making activity that is commonly carried out manually and that requires a large amount of physical effort and that has problems within the design of brick-making such as the model and drying, which is why the development of Ecological bricks which is today a new improvement to reuse plastics. The problem of manufacturing time for ecological bricks, as well as their quality, can be overcome with the elaboration of a machine and the design of the machine to process bricks based on PET-COPPER is proposed. To be applied by construction companies or artisan companies, to improve the production of a new model of mold that can withstand the compaction and heating process. The development of the machine will help to generate bricks based on PET to minimize contamination and copper, which as a material is an anti-pathogenic agent to prevent virus infections.

关键词： Design Copper anti pathogen Block machine PET

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Applying Negative Association Rule Utilizing Unsupervised Machine Learning Models 1

引用

1st International Conference on Mathematical Modeling and Computational Science, ICMMCS 2020

作者： Salih, Saddam Raheem Nassief, Amaal Mohi Department of Information Technologies Directorate General of Education in Diyala Baghdad Iraq Department of Mathematics College of Science University of Diyala Baghdad Iraq

ISBN: (数字)9789813343894

ISBN: (纸本)9789813343887

Association rules are described as the relationships between objects in the database, and these rules are broadly investigated in the analysis of shopping baskets, to examine the influence of one item on the another in the same transaction. In this paper, a novel technique is suggested that can be the ability to mine thrilling negative association rules between items in the transaction database, by extracting knowledge from that database depending on the purchased quantities. Two clustering techniques are examined in this paper, which are the density-based spatial clustering and K-means of applications with noise (DBSCAN) techniques. The outcomes of these methods are compared to the outcomes of extracting negative association rules without any domain knowledge. The utilization of DBSCAN clustering technique has given better negative association rule mining outcomes of 40,862 rules, with a rate of 0.209% support and 91.840% confidence, when examining on a real-life transaction database. Mining negative association rules depends on the domain knowledge extracted utilizing the K-means clustering technique that has 17,801 rules with a rate of 0.189% support and 85.835% confidence, while mining negative rules without any domain knowledge results in 90,655 rules with rate support of 0.122% and rate confidence of 99.365%, utilizing the same database. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Association rules

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Speaker Identification in Spoken Language Mismatch Condition: An Experimental Study 1

引用

3rd International Symposium on Signal and Image Processing, ISSIP 2020

作者： Basu, Joyanta Majumder, Swanirbhar CDAC Kolkata Salt Lake Sector—V Bidhannagar Kolkata India Department of Information Technology Tripura University Suryamaninagar Tripura India

ISBN: (数字)9789813369665

ISBN: (纸本)9789813369658

This paper describes the impact of spoken language variation in a multilingual speaker identification (SID) system. The development of speech technology applications in low resource languages (LRL) is challenging due to the unavailability of proper speech corpus. This paper illustrates an experimental study of SID on Eastern and Northeastern (E&NE) Indian languages in language mismatch conditions. For this purpose, several experiments are carried out using the LRL data to build speaker identification models. Here, spectral features are explored for investigating the presence of speaker-specific information. Mel frequency cepstral coefficients (MFCCs) and shifted delta cepstral (SDC) are used for representing the spectral information. Gaussian mixture model (GMM) and support vector machine (SVM)-based models are developed to represent the speaker-specific information captured through the spectral features. Apart from that, to build the modern SID i-vectors, time delay neural networks (TDNN), and recurrent neural network with long short-term memory (LSTM-RNN) have been considered. For the evaluation, equal error rate (EER) has been used as a performance matrix of the SID system. Performances of the developed systems are analyzed with native and non-native corpus in terms of speaker identification (SID) accuracy. The best SID performances are observed to be EER 10.52% after the corpus fusion mechanism. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Evaluation of Swarm Supervision Complexity

Evaluation of Swarm Supervision Complexity

引用

4th International Conference on intelligent Human systems Integration (IHSI) - Integrating People and intelligent systems

作者： Lindner, Sebastian Schulte, Axel Univ Bundeswehr Munich UBM Inst Flight Syst IFS Werner Heisenberg Weg 39 D-85577 Neubiberg Germany

ISBN: (纸本)9783030680176;9783030680169

Swarming is an operational concept to enable multi-robot guidance. With a human-swarm interface, called the avatar-agent, the complexity of guiding multiple unmanned vehicles (UVs) becomes independent of size. We experimentally verified in a human-in-the-loop experiment that this operational concept keeps the demand for attention of the human supervisor low. With a questionnaire we evaluated, that the avatar, as single-point-of-contact, maintained trust in automation and transparency of task execution.

关键词： Swarming Multi-robot guidance Human-in-the-loop Manned-unmanned teaming

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Emotion Recognition Through Human Conversation Using Machine Learning Techniques 1

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International Conference on Machine Intelligence and Soft computing, ICMISC 2020

作者： Sekhar, Ch. Rao, M. Srinivasa Nayani, A. S. Keerthi Bhattacharyya, Debnath Department of CSE Vignan’s Institute of Information Technology Visakhapatnam India Department of ECE Matrusri Engineering College Hyderabad India Department of CSE KL Deemed to be University Guntur Andhra Pradesh India

ISBN: (数字)9789811595165

ISBN: (纸本)9789811595158

Emotion recognition will perform a hopeful role in the field of artificial intelligence, uniquely in the case of human–machine interface development. It is the process of recognizing and analyzing the emotion of chat and text, i.e., moods of the people can be easily found, and this process can be used in various social networking websites and various business-oriented applications. The mood of the person will be confirmed by making proper observations, i.e., by asking multiple questions until his/her situation is correctly recognized. Based on his/her answers, it tries to refresh his/her mind if he/she is in a bad mood (mild) by providing the refreshments based on the interests of the person that were gathered initially. The proposed system goes about as a choice emotionally supportive network and will demonstrate to be a guide for the doctors with the analysis. The user expresses his or her feelings, and the Chatbot replies accordingly. Using Python packages, NLTK and Flair, we analyze the intensity of the emotion. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Emotion Recognition

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