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Semantic Approach for the classification of Knowledge representation and reasoning analysis

Semantic Approach for the classification of Knowledge repres...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Karnam Akhil A.Brahmananda Redddy Ragireddy Rahul Reddy Abhinav Borad Department of Computer Science and Engineering VnrVignana Jyothi Institute of Engineering And Technology Hyderabad India Department of Engineering Manufacturing La Trobe University Australia Department of Computer Science and Engineering Malla Reddy Engineering College Hyderabad India

A form of artificial intelligence (AI) that is advanced and involves rules that are applied to data to simulate a person's thought process in a particular area, knowledge engineering can be described as an advanced form of artificial intelligence (AI). Knowledge engineering had evolved from its original form when it focused on transferring and analyzing knowledge from human problem-solvers into computer programs that could do the same. .It is essential to realize that transfer processing has limitations because humans make decisions differently than machines do. Non-linear thinking and analogous reasoning, often not logical, have not been considered in this *** is becoming increasingly common today to use a modeling process for knowledge engineering, which creates a system that can reach the same conclusions as experts without following the same path or acquiring the same information from the same sources. In this paper, we aim to represent Knowledge engineering to transform knowledge into software that makes decisions similar to those made by human experts, such as financial advisors, whose decisions are based on their ***, it is expected that human experts will be replaced by knowledge engineering in decision support software

关键词： Knowledge engineering Computational modeling Semantics Transforms Knowledge representation Software Cognition

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Text Classification Using Feature Extraction and Classification Model

Text Classification Using Feature Extraction and Classificat...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Anirudh Rana Vishal Bharti Department Of Computer Science and Engineering Chandigarh University Gharuan

Sentiment analysis represents context-based text mining that classifies and abstracts particular information from the source data, and helps to interpret people's feelings. It adopts the NLP approach to classify people's opinions regarding goods or feedback. Sentiment analysis handles people's opinion and behavior in the context of feelings and attitudes concerning an episode or incidence. There are many domains that use opinion mining widely such as commercial goods review, social media study and film evaluation and so on. Semantic analysis provides major support in building recommendation frameworks. User provides textual feedback such as web-based reviews, remarks or responses on social networking and business sites. Machine learning is a common approach used for classification. This project devises a nascent methodology through the integration of dissimilar frameworks. This work applies Random Forest as a feature extraction method for taking out crucial features of the data suite. The K-mean clustering is enforced which can cluster similar type of features and in the last voting classifier is applied. Voting classifier consists of LR, KNN, SVM models. Python is the tool used in this work for the implementation of the devised methodology and evaluates new methodology with respect to some global performance indices.

关键词： Support vector machines Text mining Sentiment analysis Social networking (online) Text categorization Semantics Transforms

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Movie Recommendation System Using Machine Learning Techniques

Movie Recommendation System Using Machine Learning Technique...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ritika Roy Vandana Rai S. Rakesh Kumar School of Computing Science and Engineering Galgotias University India

Film Proposals outlines give a program to help clients in social affair clients with equivalent interests. This makes limited time structures a necessary piece of online locales and business applications. This work centers around music proposition programs whose fundamental object is to advance a proposed film however a supportive of based proposition program. This proposition system will gather information about the client's predisposition of the different movies two times, decidedly or expressly. Securing of client information frequently includes perceiving client conduct, like watching films. Of course, unequivocal insurance incorporates gathering evaluation or client *** paper talks about film proposals. The film's suggestion is significant in our public life in view of its power in giving better diversion. Such a framework can propose a bunch of motion pictures for clients in light of their inclinations, or fame films. The proposal framework is utilized to recommend things you can purchase or see. They direct clients towards them those things that can address their issues by removing a lot of data. Suggestion framework, or a proposal framework (some of the time it changes the 'framework' in a similar sense as a discussion or motor), is a little classification of data.A separating framework that looks to anticipate the "normal" or "inclinations" a client can appoint to an article. They are principally utilized in business applications. MOVREC likewise assists clients with tracking down motion pictures of their decision in view of film data of different clients in a powerful and productive manner without investing a ton of energy in pointless perusing.

关键词： Measurement Technological innovation Tracking Computational modeling Motion pictures Proposals Task analysis

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Characteristics of Fast Electric Field Generated by Negative Lightning in Northern Peninsular Malaysia. 3

Characteristics of Fast Electric Field Generated by Negative...

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3rd international conference on Emerging Electrical Energy, Electronics and computing technologies 2021, ICE4CT 2021

作者： Hwang, Yong-Jia Wooi, Chin-Leong Ahmad, Mohd Riduan Bin Nabipour-Afrouzi, Hadi Faculty of Electrical Engineering Technology Pauh Putra Main Campus Universiti Malaysia Perlis Perlis Arau02600 Malaysia Durian Tunggal Melaka76100 Malaysia Faculty of Engineering Computing and Science Swinburbe University of Technology Sarawak Kuching93350 Malaysia

The development and advancement of lightning locating system (LLS) reaches until 2021 and the characteristics of lightning can be studied by observing the electric field produced by lightning. There are several studies proven that preliminary breakdown pulses (PBPs) are always preceded the return stroke, the duration of the PBPs and the time for the PBPs preceded the return stroke tend to be different depends on the types of lightning, regions, and location. This paper observed and recorded the fast electric field changes of negative cloud to ground lightning (CG). Total of ten sample data was captured on 13 May 2021 and selected to be analyzed in detail. Information such as rise time to peak, duration of preliminary breakdown pulses has been recorded. The captured result is compared with the results from other papers. The results show that the PBPs duration are very similar when compared with local results which shows an average of 2.347 ms. While the average time for PBPs to precede the first return stroke is around 9.44 ms and the rise time from 10% to 90% peak of first return stroke shows an average time of 4.86 μs © Published under licence by IOP Publishing Ltd.

关键词： Lightning

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IEEE ICCI*CC Series in Year 20: Latest advances in Cognitive computing (Plenary Panel Report-II) 20

IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive...

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20th IEEE international conference on Cognitive Informatics and Cognitive computing, ICCI*CC 2021

作者： Kinsner, Witold Zhu, Haibin Baciu, George Luo, Guiming Rubio, Fernando Sui, Jie Huang, Runhe Hiraishi, Hironori Peng, Jun Chen, Liang Univ. of Manitoba FI2CICC Dept of ECE Winnipeg Canada Nipissing University FI2CICC ON Canada Hong Kong Polytechnic University FI2CICC Dept. of Computing Hong Kong Tsinghua University FI2CICC Software Institute Beijing China Complutense Univ. of Madrid FI2CICC Dept. of Computer Systems and Computation Spain Aberdeen University FI2CICC Aberdeen United Kingdom Hosei University FI2CICC Faculty of Computer and Information Sciences Japan Ashikaga University Faculty of Engineering Tochigi326-8558 Japan Chongqing Univ. of Science and Technology FI2CICC School of AI Chongqing China Univ. of Northern British Columbia Dept. of Computer Science BC Canada

ISBN: (纸本)9781665421195

Cognitive computing (CC) is a contemporary field of fundamental intelligence theories and general AI technologies triggered by the transdisciplinary development in intelligence, computer, brain, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part II) on the theoretical foundations of CI/CC and recent breakthroughs in AI engineering reported in the 20th IEEE international ICCI*CC conference (ICCI*CC'21). The latest advances in CI and CC towards general AI are presented by twenty-Two distinguished panelists. Strategic AI engineering applications in CI, CC, and cognitive systems are elaborated for abstract intelligence, cognitive robots, autonomous systems, intelligent vehicles, and safety-And-mission-critical systems. © 2021 IEEE.

关键词： Cognitive systems

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Implementation of Adaptive Bit Decision Point to Improve Receiver Performance in Li-Fi System

Implementation of Adaptive Bit Decision Point to Improve Rec...

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international conference on Informatics and computing (ICIC)

作者： Mudrik Alaydrus Juan Salao Biantong Department of Electrical Engineering Universitas Mercu Buana Jakarta Indonesia Department of Computer Science and Engineering Seoul National University of Science and Technology Republic of Korea

The growing demand for high-reliability and low-latency wireless links requires increasingly modern wireless technologies predicted in 5G and beyond. In general, wireless communication devices currently use Radio Frequency (RF) in the data transmission process, so the exponential increase continues to make the radio spectrum below 10 GHz insufficient. While most of the mobile data traffic is consumed indoors, where Light Fidelity (Li-Fi) associated with Visible Light Communication (VLC) offers many special advantages and effective solutions to various wireless communication problems. Based on the current development of Li-Fi, researchers see that the receiver is very dependent on the processes that occur in the detector in interpreting changes in light intensity (signal) as data. For this reason, through this research, the researcher proposes an Adaptive Bit Decision Point (BDP) algorithm that allows the receiver to adapt to changes in the intensity of light captured by the detector, both changes to Ambien Light and changes to distance and angle to improve data reception performance on the Li-Fi system. The results show that the application of the Adaptive Bit Decision Point algorithm on the receiver of the Li-Fi system can provide a very significant performance increase

关键词： Wireless communication Radio frequency Wireless sensor networks Adaptive systems Radio transmitters Receivers Detectors

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Biometric Facial Security System with Telemetry Module

Biometric Facial Security System with Telemetry Module

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Brian Meneses-Claudio Luis Tarmeñ o-Bernuy Melissa Yauri-Machaca Enrique Lee Huamaní Facultad de Negocios Universidad Tecnol&#x00F3 gica del Per&#x00FA Lima Per&#x00FA Research and Technology Direction Business on Making Technologies Lima Per&#x00FA Image Processing Research Laboratory (INTI-Lab) Universidad de Ciencias y Humanidades Lima Per&#x00FA

ISBN: (数字)9781665411202

ISBN: (纸本)9781665411219

Over the years the human being has sought greater security for their personal things, this has led them to develop automatic systems of detection, recognition, and fast and reliable monitoring, becoming an important tool for many people because they can identify or verify through biometric data the identity of a person and thereby guarantee access to a place. Traditional methods such as passwords, cards, among others, involve some risks such as loss, forgetfulness, cloning or theft, which is why they are not very secure. Currently, biometric facial security systems have contributed to the identification of people, access to private sites and all places that need security and order with the help of computerized computers that perform facial biometric recognition, exclusively based on images of human faces for their function, being a technology that is still being developed in the field of research. In view of this problem, this article will implement a biometric facial security system with telemetry module to detect the face of users and according to a previous evaluation allow or not access to a certain area or information safely. By implementing the facial security system, you have a more accurate detection of people's faces with an efficiency of 97.75%, so the system works in the best way by identifying each person when entering.

关键词： Biometrics (access control) Electronic components Passwords Programming Security Telemetry Object recognition

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Adaptation of AL-TST active learning model in hybrid classroom: Findings from teaching during COVID-19 pandemic in Egypt 26

Adaptation of AL-TST active learning model in hybrid classro...

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26th international conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Hasnine, Mohammd Nehal Ueda, Hiroshi Ahmed, Mahmoud Mohamed Hussien Research Center for Computing and Multimedia Studies Hosei University Tokyo184-8584 Japan Educational Technology Department South Valley University Qena83523 Egypt

Since COVID-19 began, the ways of teaching and learning have changed drastically. Traditional teaching methods are shifted to technology-mediated methodologies such as asynchronous online learning, hybrid learning, blended learning, hy-flex learning, on-demand learning, and competency-based online learning. Due to the COVID-19 pandemic, traditional active learning models, compelling but complex, are assumed not to fit in the hybrid classroom because of the affordance and integration of various distance learning technologies. Hence, in the research, a conceptual active learning model for a hybrid classroom, namely AL-TST (Active Learning-Theory, Strategy, Technology), is used to deliver a STEM course in an Egyptian university. The course was an 8-week-long course designed for 3rd grade (i.e., university junior) students enrolled in the university. At the beginning of the course, the instructor created student-centered lecture contents using the adopted AL-TST model in a hybrid environment. data (N=76) were collected using the university's learning management system (LMS). Students' Course Work Grades, Final Exam Grades and Total Exam Grades are analyzed using a one-sample t-test. The analysis indicated no significant result in students' Course Work Grades, Final Exam Grades and Total Exam Grades. However, the findings indicated some valuable educational insights, such as improving a conceptual active learning model, the perception of course design, and teaching during the crisis. © 2022 The Authors. Published by Elsevier B.V.

关键词： COVID-19

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Speech Emotion Recognition Using Deep Learning Hybrid Models

Speech Emotion Recognition Using Deep Learning Hybrid Models

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Emerging technologies in Electronics, computing and Communication (ICETECC), international conference on

作者： Jamsher Bhanbhro Shahnawaz Talpur Asif Aziz Memon Department of Computer Systems Engineering Mehran University of Engineering and Technology Jamshoro Pakistan Department of Computer Science Dawood University of Engineering and Technology Karachi Pakistan

Speech Emotion Recognition (SER) has been essential to Human-Computer Interaction (HCI) and other complex speech processing systems over the past decade. Due to the emotive differences between different speakers, SER is a complex and challenging process. The features retrieved from speech signals are crucial to SER systems’ performance. It is still challenging to develop efficient feature extracting and classification models. This study suggested hybrid deep learning models for accurately extracting crucial features and enhancing predictions with higher probabilities. Initially, the Mel spectrogram’s temporal features are trained using a combination of stacked Convolutional Neural Networks (CNN) & Long-term short memory (LSTM). The said model performs well. For enhancing the speech, samples are initially preprocessed using data improvement and dataset balancing techniques. The RAVDNESS dataset is used in this study which contains 1440 samples of audio in North American English accent. The strength of the CNN algorithm is used for obtaining spatial features and sequence encoding conversion, which generates accuracy above 93.9% for the model on mentioned data set when classifying emotions into one of eight categories. The model is generalized using Additive white Gaussian noise (AWGN) and Dropout techniques.

关键词： Human computer interaction Deep learning Emotion recognition AWGN Computational modeling Speech recognition Speech enhancement

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Examining consumers’ willingness to participate in aggregated demand response: An exploratory case study in Spain

Examining consumers’ willingness to participate in aggregat...

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IEEE international conference on Smart Grid Communications (SmartGridComm)

作者： Esther Palomar Carlos Cruz Ignacio Bravo Marina Hernandez-Grau Dept. of Computer Science University of Alcala Madrid Spain Dept. of Electronics University of Alcala Madrid Spain

ISBN: (数字)9798350318555

ISBN: (纸本)9798350318562

This paper presents a study which links electricity consumers’ technical confidence, awareness, and motivating factors affecting the adoption of demand response (DR) programmes, aggregation and other domestic energy efficiency measures. The aim is to statistically correlate these factors (and variables) to better understand their role played in the effectiveness of future design and deployment of sustainable energy community initiatives. The data for this study is collected from 75 DR pilot participants’ responses to an online categorical multiple-response survey (***/en/) carried out between November 2022 and March 2023 in Spain. We apply chi-square test of association, contingency tables as well as clustering and k-nearest neighbours algorithms to the dataset, amongst other statistical and machine learning methods, paying special attention to the data preprocessing. Despite the lack of strong associations between those variables as revealed by our analysis, it showcases a number of patterns in which factors like the consumer motivation dominate the effective participation in DR initiatives and pilots. Three conclusions can be drawn. Consumer motivation to participate in DR programmes is heterogeneous, multivariate, and non-normally distributed. The survey responses point to a flourishing of technical competence among the general consumer, though insufficient to foster proactive involvement in DR initiatives. Moreover, unsure or undecided behaviours prevail over non-cooperation, providing a window of opportunity for awareness raising strategies. To that end, we encourage strategies that relate to raise a social and community identity and awareness of the tangible benefits achieved by using more efficient technologies and engaging in new energy initiatives to motivate the change.

关键词： Surveys Accuracy Statistical analysis Electricity Clustering algorithms Predictive models Prediction algorithms Demand response Smart grids Pattern recognition

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