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1st International Conference on Computational science and Technology, ICCST 2022

作者： Sharmikha Sree, R. Sivaranjani, D. Kavya Keerthana, M. Sri Sairam Engineering College Dept. of Computer Science and Engineering Tamil Nadu Chennai602109 India

ISBN: (纸本)9781665476553

The BLYNK IOT platform, which is a mobile application, is used to create a street light control system in this project. With less staff and far less power being used, this project hopes to conserve fuel. This method recognizes the level of illumination or darkness in the environment when using a light dependent resistor (LDR), an infrared sensor (IR sensor) to detect movement of objects in the environment with LED (Light Emitting Diode), and a WIFI module for data transport. If there are no vehicles or trespassers passing through at night, the planned work avoids power waste. A watersensor is also used to detect water logging in certainregions. If constructed, this smart street light will outperform the current system. © 2022 IEEE.

关键词： Internet of things

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Revolutionizing Requirements Elicitation: Deep Learning-Based Classification of Functional and Non-Functional Requirements

Revolutionizing Requirements Elicitation: Deep Learning-Base...

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Grace Hanna Nathan Boyar Nathan Garay Mina Maleki Dept. of Electrical & Computer Engineering & Computer Science University of Detroit Mercy Detroit MI United States

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

The requirements elicitation phase in the software development life cycle (SDLC) is both critical and challenging, especially in the context of big data and rapid technological advancement. Traditional approaches like workshops and proto-typing, while useful, often struggle to keep pace with the massive data volumes and rapidly changing user demands characteristic of modern technology. This paper introduces a data-driven approach that utilizes deep learning (DL) and natural language processing (NLP) to enhance the requirements elicitation process by extracting requirements and classifying them into functional and non-functional categories. Our research involves a deep neural network (DNN) trained on a large dataset of transcriptions from client/user stories. This DNN can identify whether a specific text represents a functional requirement, a non-functional requirement, or neither. Our approach shows a marked improvement over previous methods, with a 33% increase in accuracy and an 18% increase in the F1 score. These results indicate the capability for deep learning techniques to play a vital role in elicitation.

关键词： Deep learning Accuracy Conferences Artificial neural networks Big Data Natural language processing Real-time systems Pattern recognition Software development management

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SIGN LANGUAGE RECOGNITION USING CNN

SIGN LANGUAGE RECOGNITION USING CNN

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Sangeetha Allam Allam Balaram Yedavalli Venkata Raghava Rao Ajmeera Kiran Hema Valpadasu Soleti Navya Department of Computer Science & Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad CSE AIML AVNIET JNTUH AIML DEPT Department of Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana Department of Computer Science and Information Technology MLR Institute of Technology

Conversation between both the deaf and the general population is becoming exceedingly challenging, and there is no reputable translator accessible in society to aid. This program enables real-time voice and signal language translation, in distinctive: 1. recognizing male or female signals 2. Establishing a system learning model for interpreting image-to-text data. 3. Seeking innovative terms Four. Constructing Sentences 5. After creating all of the content, 6. Is to obtain the audio output. The goal of this research is to develop a framework for a potential sign language interpreter who could also translate conversations between sign languages into written and spoken English. If there had a translator like this, it would be much easier for many deaf and partially deaf people to interact with others in everyday situations

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A Fast and More Accurate Seed-and-Extension Density-Based Clustering Algorithm

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IEEE Transactions on Knowledge and Data engineering 2023年第6期35卷 5458-5471页

作者： Tung, Ming-Hao Chen, Yi-Ping Phoebe Liu, Chen-Yu Liao, Chung-Shou Research and Development Micron Technology Inc Hsinchu Taiwan Department of Computer Science and Information Technology La Trobe University Melbourne Australia Dept. Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu Taiwan Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu Taiwan

Clustering algorithms have been widely studied in many scientific areas, such as data mining, knowledge discovery, bioinformatics and machine learning. A density-based clustering algorithm, called density peaks (DP), which was proposed by Rodriguez and Laio, outperforms almost all other approaches. Although the DP algorithm performs well in many cases, there is still room for improvement in the precision of its output clusters as well as the quality of the selected centers. In this study, we propose a more accurate clustering algorithm, seed-and-extension-based density peaks (SDP). SDP selects the centers that hold the features of their clusters while building a spanning forest, and meanwhile, constructs the output clusters in a seed-and-extension manner. Experiment results demonstrate the effectiveness of SDP, especially when dealing with clusters with relatively high densities. Precisely, we show that SDP is more accurate than the DP algorithm as well as other state-of-the-art clustering approaches concerning the quality of both output clusters and cluster centers while maintaining similar running time of the DP algorithm, particularly for a variety of time-series data. Moreover, SDP outperforms DP in the dynamic model in which data point insertion and deletion are allowed. From a practical perspective, the proposed SDP algorithm is obviously helpful to many application problems. © 1989-2012 IEEE.

关键词： Heuristic algorithms

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Higher-order interactions (Bipolar or not) in abstract argumentation: A state of the art

Journal of Applied Logics

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Journal of Applied Logics 2021年第6期8卷 1339-1435页

作者： Cayrol, Claudette Cohen, Andrea Lagasquie-Schiex, Marie-Christine IRIT Université Toulouse 3 Toulouse France Institute for Computer Science and Engineering CONICET-UNS Dept. of Computer Science and Engineering Universidad Nacional del Sur Bahía Blanca Argentina

In Dung’s seminal work, an argumentation framework was defined by a set of abstract arguments and a binary (and also abstract) relation between these arguments, called attack relation and expressing conflicts between arguments. Due to its simplicity and the power of its abstraction, this representation has been intensively used by the community for over 25 years. Another advantage of this approach is the ease with which we can extend the framework, weighting arguments or attacks, using priorities or pre-orderings on the sets of arguments, considering that these interactions are no longer binary ones over the set of arguments (e.g. collective attacks), adding new kinds of interactions (e.g. supports), and proposing that the targets of these interactions can also be interactions themselves (i.e. higher-order interactions). These last two points are the core of this chapter, in which we present a survey of the proposed approaches existing around the notion of higher-order interactions (attacks and supports) in an abstract argumentation framework. © 2021, College Publications. All rights reserved.

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Using Machine Learning and Google Earth Engine to Understand Land Use and Land Cover Classifications and NO2 Levels in California

Using Machine Learning and Google Earth Engine to Understand...

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IEEE International Conference on Electro-Information Technology

作者： Benyamain Yacoob Ethan Scheys Eyiara Oladipo Andre Price Shadi Banitaan Dept. of Electrical & Computer Engineering & Computer Science University of Detroit Mercy Detroit MI United States

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

Anthropogenic activities release pollutants into the air, which can negatively affect human health and the environment. One such pollutant is nitrogen dioxide (NO 2 ), which can contribute to smog formation, decreased crop growth and yield, and respiratory damage. This study aimed to find a relationship between land use/land cover (LULC) classifications and NO 2 levels in the air. We used Google Earth Engine (GEE) to collect LULC and air quality data using the Google Dynamic World and the Sentinel-5P NRTI NO 2 datasets. We focused on Pasadena, California, as it provided a good demonstration of an urban area surrounded by greenery, allowing for an adequate analysis of both forms of landscape and their impact on air quality. Random forest (RF) and decision tree (DT) classifiers were used on the provided datasets, with the estimated probability of complete coverage for each LULC type being the input features and the NO 2 density being the output label, measured in mol/m 2 . Our output labels were then discretized, classifying the categories into high and low NO 2 . The machine learning classifier found a correlative relationship between LULC and NO 2 levels, as signified by our modeled accuracy outputting a value of 85%, with an average f1 score of 86%. We performed 10-fold cross-validation to enhance the reliability of model evaluation. The results from this study suggest that machine learning models can be used to predict the changes in air quality based on changes in LULC from anthropogenic activities. With future studies confirming this relationship, inner-city green spaces may benefit mental and physical well-being.

关键词： Earth Atmospheric modeling Green products Urban areas Predictive models Air quality Internet

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State-Space Average Modeling Based PID Control for Three Novel Topologies of DC-DC Boost Converter

State-Space Average Modeling Based PID Control for Three Nov...

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2022 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2022

作者： Zaman, Meem Tasfia Fu, Xingang Challoo, Rajab North South Univeristy Dept. of Electrical and Computer Engineering Dhaka Bangladesh Texas A&m University-Kingsville Dept. of Electrical Engineering and Computer Science Kingsville United States

ISBN: (纸本)9798350311563

The objectives of this paper are to obtain state-space average models of the three new topologies of the DC-DC boost converter and apply Proportional Integral Derivative (PID) controller on them to analyze their performance. One of the most important applications of DC-DC boost converter in modern world is in Photovoltaic (PV) systems. It boosts up the low DC voltage coming from the PV panels to a high DC bus voltage and it drives the DC-AC inverter. Thus, the efficiency and the control of it play very important role in this entire process. Recently, some new topologies have been proposed to improve the efficiency of this converter. In order to inspect these topologies, proper mathematical modeling is required, and the state-space average model is one of the efficient techniques to do that. Among various control techniques, PID controller is widely used to get the best-desired output voltage of this converter. To get the expected result, MATLAB Simulink models and MATLAB m-file code will be used in this paper. © 2022 IEEE.

关键词： Three term control systems

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Software Fault Prediction Using Optimal Classifier Selection: An Ensemble Approach

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Procedia computer science 2024年 235卷 2965-2974页

作者： Bikash Agrawalla B Ramachandra Reddy Dept. of Computer Science and Engineering National Institute of Technology Jamshedpur Jharkhand India 831014.

Fault prediction is the process of using data analysis and machine learning models to anticipate potential defects or faults in the software system. Using only the base machine learning models for software fault prediction leads to limited performance, difficulty in handling non-linear relationships and imbalanced data, inadequate feature representation, and limited complexity handling. Hence, in order to overcome these challenges, this paper proposes a new technique for the selection of classifiers that forms a heterogeneous ensemble. The main goal is to remove or trim out the classifiers that show poor performance compared to the other base classifiers, which can result into a more effective ensemble and can produce better results. The algorithm proposed in this paper finds a set of classifiers that can perform better than using all the classifiers. The challenge that was faced was how to identify the poor-performing classifiers. This challenge is dealt with by performing an experiment using different threshold values to choose the trimmed set of classifiers. For evaluation of the proposed model, 8 different benchmark software fault datasets were used, which are taken from PROMISE and the Apache repository, and AUC is used as the performance measure. The results obtained after the experimental analysis demonstrate the effectiveness of our algorithm compared to the traditional approaches, which used all the base classifiers. There is a significant increase in the AUC values for 6 datasets out of 8, while using the average of probabilities and majority voting, it was seen that there is improvement in 7 out of 8 datasets used. The best-performing dataset by using the average of probabilities is ARC, where the AUC values increase from 0.6505 to 0.694, and while using majority voting, the best-performing dataset is XALAN, where the AUC values increase from 0.5455 to 0.679. From this, it can be seen that the proposed ensemble approach achieved higher AUC values for the

关键词： Software Fault Prediction Logistic Regression Decision Trees Ensemble Learning Majority Voting

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Towards Detection of Hirschsprung's Disease using Dielectric Properties: Feasibility in thin mice colon

Towards Detection of Hirschsprung's Disease using Dielectric...

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2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2023

作者： Samaddar, Poulami Gaddam, Sunil Gopalakrishnan, Keerthy Cherukuri, Akhila Modi, Vaishnavi Dey, Shuvashis Roy, Sayan Mitra, Dipankar Arunachalam, Shivaram P. Division of Gastroenterology and Hepatology Dept. of Medicine RochesterMN United States North Dakota State University Dept. of Electrical and Computer Engineering FargoND United States South Dakota Mines Dept. of Electrical Engineering and Computer Science Rapid CitySD United States University of Wisconsin-La Crosse Dept. of Computer Science La CrosseWI United States Mayo Clinic Department of Radiology RochesterMN United States

ISBN: (纸本)9781665442282

Hirschsprung disease is a congenital disability due to the defect in migration of colonic ganglion. Definitive diagnosis is based on the findings of histopathological evaluation of rectal biopsies which can be highly variable. Post-op perforations and bleeding are major complications of rectal biopsy. Difficulty in establishing a definitive diagnosis leads to severe complications. Research studies on the myoelectrical activity in Hirschsprung's disease have shown disorganized electrical activities and variations in spontaneous membrane activity in the affected colon. Electrical properties can be measured as complex permittivity using dielectric spectroscopy as an attempt to diagnose Hirschsprung's disease. Dielectric spectroscopy measurements on thin biological samples offer challenges due to the requirements of sample size and probe positioning. In this work, the feasibility of dielectric property measurement of thin colon of early postnatal mice using the coaxial probe method is investigated. Complex permittivity of colon tissue was calculated from simulated return loss using a previously published mathematical model and compared with measured results. The results showed very good agreement from 500 MHz to 8 GHz range and demonstrate the potential for using dielectric properties as a biomarker for Hirschsprung disease. © 2023 IEEE.

关键词： Electromagnetic simulation

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Tangispeak: A Digital Game Based Language Learning System by Using Augmented Reality Technology and Tangible Objects 12

Tangispeak: A Digital Game Based Language Learning System by...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Ongoro, Catherine Akoth Fanjiang, Yong-Yi Hung, Chi-Huang Lin, Bing-Jun Lee, Mu-En Fu Jen Catholic University Graduate Institute of Applied Science and Engineering New Taipei City Taiwan Fu Jen Catholic University Graduate Institute of Applied Science and Engineering Dept. of Computer Science and Information Engineering New Taipei City Taiwan Fu Jen Catholic University Lee-Ming Institute of Technology Graduate Institute of Applied Science and Engineering Dept. of Information Technology New Taipei City Taiwan Fu Jen Catholic University Dept. of Computer Science and Information Engineering New Taipei City Taiwan

ISBN: (纸本)9798350340181

In this study, Tangispeak is introduced as a language learning system for preschoolers, incorporating Digital Game-Based learning (DGBL), Augmented Reality (AR), and tangible objects. It aims to enhance English language skills through interactive and fun experiences. Tangispeak utilizes a micro-controller, gameboard, Unity 3D, and Vuforia database platform, offering scrambling and speaking steps for word formation and pronunciation. The system provides rewards, hints, and timing to engage learners while fostering cognitive and innovative skills. © 2023 IEEE.

关键词： Augmented reality

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