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13th IEEE Symposium on computer Applications and Industrial Electronics, ISCAIE 2023

作者： Rahman, Mohammad Mustaneer Al Salem, Hassan A. Yeom, Soonja Ollington, Nadia Ollington, Robert Rahman, Md. Mujibur University of Tasmania School of ICT HobartTAS Australia Jazan University Department of Management Information Systems Al Maarefah Rd Jazan Saudi Arabia University of Tasmania Faculty of Education HobartTAS Australia University of Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350347319

Intelligent tutoring systems (ITS) are computer-based learning systems that use intelligent technologies to provide adaptive learning paths and contents to learners by considering their characteristics and needs. The learner model, which is a representation of different learner's characteristics, plays a key role in this adaptation. Research on educational psychology showed that emotion and learning have a hidden mutual relationship. If a learning process involves both cognitive and affective dimensions, then learning is like to be more usable, socially appropriate, acceptable and memorable, ultimately leading to significant improvement in the learner's performance and the learning experience. However, traditional ITS ignore emotional aspects in learner modelling mostly;instead, they focus on learners' cognition and motivation to maximise the knowledge to be achieved. The characteristics related to cognition are not well defined and primarily sparse when modelling a learner profile tailored to the learning domains. This study attempts to investigate the most common and relevant personal traits involved in the learning process based on the domains of learning. It also explores the most adopted techniques to identify such traits from log files of learners' interactions with ITS without using physical and physiological sensors (i.e., camera, EEG) to model characteristics learner profiles for ITS aiming to improve its adaptiveness and leverage personalised learning. © 2023 IEEE.

关键词： computer aided instruction

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Improved Ingredients-based Recipe Recommendation Software using Machine Learning

Improved Ingredients-based Recipe Recommendation Software us...

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IEEE International Conference on Intelligent Computing and information systems (ICICIS)

作者： Mariam Ashraf Khadija Tarek Naglaa Fathy Mariam Mahmoud Rana Emad Khadija Khaled Noran Amr Software Engineering Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt Information Systems Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt

In this paper we propose an improved recipe recommendation system that employs image recognition of food ingredients. The system is currently a mobile application that performs image recognition on uploaded or camera-captured images and recommends recipes containing the recognized ingredients. We used the ResNet-V2 architecture to build a convolutional neural network model for image recognition, which was able to identify 33 different food ingredients with an accuracy rate of 89%. The recommendation system uses the identified ingredient labels, as well as user preferences and restrictions, to display a list of recipes containing the identified ingredients. This feature allows users to discover new and exciting recipes based on the ingredients they currently have at home, without having to worry about dietary restrictions or other preferences. Overall, our system provides a convenient and personalized way for users to discover and prepare delicious meals based on their unique needs and preferences.

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UWB Channel Modeling and Simulation with Continuous Frequency Response

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China Communications 2022年第11期19卷 88-98页

作者： Kai Mao Qiuming Zhu Xijuan Ye Ruirui Feng Fuqiao Duan Yang Miao Maozhong Song The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space College of Electronic and Information EngineeringNanjing University of Aeronautics and AstronauticsNanjing 211106China The Radio Systems Faculty of Electrical Engineering Computer Science and Mathematics(EEMCS)University of Twentethe Netherlands

Ultra-wideband(UWB)technology is a prospective technology for high-rate transmission and accurate localization in the future communication ***-of-art channel modeling approaches usually divide the UWB channel into several sub-band channels and model them *** considering frequency-dependent channel parameters,a novel analytical UWB channel model with continuous frequency response is *** composite effect of all frequency components within the UWB channel on the channel impulse response(CIR)of delay domain is derived based on the continuous channel transfer function(CTF)of frequency *** this basis,a closed-form simulation model for UWB channels and geometry-based parameter calculation method are developed,which can guarantee the continuity of channel characteristics on the frequency domain and greatly reduce the simulation ***,the proposed method is applied to generate UWB channel with 2 GHz bandwidth at sub-6GHz and millimeter wave(mmWave)bands,*** channel measurements are also carried out to validate the proposed *** simulated CIR and power gain are shown to be in good agreement with the measurement ***,the comparison results of power gain and Doppler power spectral density(DPSD)show that the proposed UWB channel model Received:Apr.23,2022 Revised:Jun.09,2022 Editor:Wei Fan achieves a good balance between the simulation accuracy and efficiency.

关键词： ultra-wideband(UWB)channel continuous frequency response frequency-dependent channel parameters simulation model channel measurement

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Efficient Privacy-Preserving Edge-based Dynamic Aggregation Query Over Crowdsensed Data

Efficient Privacy-Preserving Edge-based Dynamic Aggregation ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yu, Yantao Guan, Yunguo Xue, Xiaoping Lu, Rongxing Tongji University College of Electronic and Information Engineering Shanghai201804 China Eastern Michigan University School of Information Security and Applied Computing YpsilantiMI48197 United States University of New Brunswick Faculty of Computer Science FrederictionNBE3B 5A3 Canada

ISBN: (纸本)9798350351255

Aggregation query, which is one of the most crucial data analysis tools applied to vehicular crowdsensed data, is expected to extract valuable insights and support smart decision-making. Meanwhile, the edge servers are deployed to cope with the escalation of service scale, which however raises privacy concerns about the query requests and reported data. Previously reported privacy-preserving aggregation query schemes are either tailored for static datasets or lack an index structure for deployed queries, which makes them impractical in vehicular crowdsensing (VCS) scenarios characterized by large volumes of real-time data and high data update frequencies. To tackle these challenges, we propose an efficient privacy-preserving edge-based dynamic aggregation query scheme with an encrypted tree-based index. Concretely, we first design two building blocks, namely the balanced spatial encoding quadtree (BSEQtree) and a predicate encryption scheme for membership testing (PEMT), which enable the edge servers to obliviously match data and queries by traversal over the encrypted BSEQtree. Based on the above blocks and arithmetic secret sharing (ASS), we construct our scheme, in which the edge servers can efficiently and securely aggregate newly reported data to related query results. Our security analysis shows the privacy preservation of our scheme, and the experiment results on a real dataset validate the efficiency of our scheme. © 2024 IEEE.

关键词： Data aggregation

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Optimizing Wastewater Treatment Plant Operations: A Machine Learning Approach for Energy Consumption and Climate Dynamics Analysis 8

Optimizing Wastewater Treatment Plant Operations: A Machine ...

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8th IET Smart Cities Symposium, SCS 2024

作者： Elshabrawy, Mahmoud Rizk, Faris H. Zaki, Ahmed Mohamed Eid, Marwa M. Takieldeen, Ali E. Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Abualigah, Laith El-Kenawy, El-Sayed M. Computer Science and Intelligent Systems Research Center Blacksburg 24060 VA United States Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura 35111 Egypt School of ICT Faculty of Engineering Design and Information & Communications Technology (EDICT) Bahrain Polytechnic PO Box 33349 Isa Town Bahrain Department of Computer Science College of Computing and Information Technology Shaqra University Shaqra 11961 Saudi Arabia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Jadara University Research Center Jadara University Jordan Applied Science Research Center Applied Science Private University Amman Jordan

ISBN: (纸本)9781837242672

Treatment plant optimization plays an important role in the implementation of eco-problem solutions. This article is going to cover how machine learning algorithms can determine energy consumption, climate, and wastewater features of electricity at a wastewater treatment plant in eastern Melbourne from 2014 to 2019. This study uses data obtained from the Melbourne Water and Airport weather station, which are freely available to carry out MLPRegressor, SVM, Linear Regression, Decision Tree Regression, random forest regression, and Nearest Neighbor. Through customary measures like MSE, RMSE, EVS, and others, the best model is adequately identified, which is the Random Forest Regressor model, which has 0.889285. (Lowest MSE) With these results, the plant treatment optimization processes, such as sampling for environmental analysis and the control of energy consumption, have improved. © The Institution of Engineering & Technology 2024.

关键词： Climate dynamics Data analysis Energy consumption Wastewater characteristics Wastewater treatment

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Collision detection and response approaches for computer muscle modelling 16

Collision detection and response approaches for computer mus...

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16th IEEE International Scientific Conference on Informatics, Informatics 2022

作者： Havlicek, Ondrej Cervenka, Martin Kohout, Josef University of West Bohemia Faculty of Applied Sciences Department of Computer Science and Engineering Pilsen Czech Republic University of West Bohemia NTIS-New Technologies for the Information Society Faculty of Applied Sciences Pilsen Czech Republic

ISBN: (纸本)9798350310344

computer muscle modelling is used for many pur-poses, from injury recovery and treatment of chronic diseases to disease prediction. These predictions often involve computing the muscle's internal forces to determine further how fast something may happen (e.g. how quickly the muscle joint wears out). During the simulation of such a model, collisions of soft and rigid bodies inevitably occur. This paper tests various state-of-the-art collision handling methods: voxelisation, one using Signed Distance Fields and one based on Bounding Volume Hierarchies. These methods are tested in the context of muscle modelling with the previously proposed position-based dynamics approach. Compared to the other options, using the Discregrid library for Signed Distance Field generation shows the best results, mainly due to its accuracy to the speed of execution ratio. In contrast to the current system, visually pleasant improvements are significant. © 2022 IEEE.

关键词： Muscle

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AI-Safe Transportation: Real-Time Incident Detection and Alerting System in Smart Cities

AI-Safe Transportation: Real-Time Incident Detection and Ale...

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IEEE International Conference on Intelligent Computing and information systems (ICICIS)

作者： Ziad Al-Agroudy Abdelrahman Mohamed Ziad Ashraf Salma Al-Sayed Walaa Gad Ziad Hassan Radwa Reda Information Systems Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt Scientific Computing Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt

Recently, with the increasing complexity of urban environments, the need for advanced technologies to ensure safe transportation in smart cities has become paramount. It has become necessary also to propose a model based on the latest technology, which can detect various road and environmental accidents in real time using live feed from street cameras. In this work, the proposed model can identify incidents such as car accidents, fires, floods, and other malfunctions in the road environment, and automatically generates two reports - one for the government to seek assistance and another for drivers to avoid the affected area and mitigate traffic congestion. The developed model utilizes state-of-the-art Deep learning techniques. It is implemented by convolutional neural networks (CNNs), for efficient processing and analysis of live video feeds. The model is trained on a comprehensive dataset of images, encompassing different types of road and environmental incidents. The trained model is subsequently integrated into a real-time monitoring system that continuously receives and analyzes live feeds from multiple cameras installed in the streets. The main features of the developed system include real-time detection and classification of road and environmental incidents, automatic generation of reports for government and users, and proactive traffic management to reduce traffic congestion in affected areas. The system is designed to work seamlessly with existing smart city infrastructure and can be easily integrated into transportation management systems. The experimental results proved that the deep learning YOLO model with a dataset licensed by MIT achieved an average accuracy of 91% in detecting various types of road and environmental incidents in real-time.

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Cybersecurity Threats in the Era of AI: Detection of Phishing Domains Through Classification Rules 2

Cybersecurity Threats in the Era of AI: Detection of Phishin...

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2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Aljaidi, Mohammad Alsarhan, Ayoub Al-Fraihat, Dimah Al-Arjan, Ahmed Igried, Bashar El-Salhi, Subhieh M. Khalid, Muhammad Al-Na'amneh, Qais Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan The Hashemite University Department of Information Technology Jordan Isra University Faculty of Information Technology Department of Software Engineering Amman11622 Jordan The Hashemite University Faculty of Prince AL-Hussein Bin Abdullah Ii of Information Technology Department of Information Technology Jordan The Hashemite University Department of Computer Science and Applications Zarqa Jordan The Hashemite University P. O Box 330127 Zarqa13133 Jordan University of Hull Department of Computer Sciences and Technology Kingston upon Hull United Kingdom Applied Science Private University Dep. of Cyber Security Amman Jordan

ISBN: (纸本)9798350373363

Phishing is a cyber threat where attackers create deceptive websites or emails to exploit individuals for fraudulent purposes such as providing sensitive information like usernames, passwords, or financial details. Hence, detecting phishing emails is of paramount importance to protect sensitive information, preventing financial losses, protecting against identity theft, and maintaining trust. This study employs machine learning techniques to classify and categorize websites that are used for phishing attacks. To achieve this, we proposed a classification model for the detection of phishing domains based on classification rules. These rules were formulated based on analyzing and distributing them into three classifiers: domain name features, search engine opinion (SEO) information of the domain, and security scans by security vendors. A total of eleven classification rules were formulated which were distributed as follows: six rules for the first classifier, four rules for the second classifier, and one rule for the third classifier. Each classifier feeds its results to the main classifier, which then evaluates the received results based on its rules. Domain names from 222 confirmed phishing URLs were used to test the suggested model. The model demonstrated exceptional performance, achieving an impressive accuracy rate of 99.1% during testing. © 2023 IEEE.

关键词： Websites

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Optimal Load Forecasting Model for Peer-to-Peer Energy Trading in Smart Grids

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computers, Materials & Continua 2022年第1期70卷 1053-1067页

作者： Lijo Jacob Varghese K.Dhayalini Suma Sira Jacob Ihsan Ali Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Electrical and Electronics Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia

Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and *** also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and *** recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction *** order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in *** proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter *** addition,MOGOA-based FS technique is utilized in the selection of optimum subset of ***,DELM-based predictive model is also applied in forecasting the load *** proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive *** inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter *** the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.

关键词： Peer to Peer energy trade smart grid load forecasting machine learning feature selection

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CervixNet: A Reward-Based Weighted Ensemble Framework for Cervical Cancer Classification 4th

CervixNet: A Reward-Based Weighted Ensemble Framework for Ce...

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4th International Conference on Frontiers in Computing and systems, COMSYS 2023

作者： Roy, Kaushiki Bhattacharjee, Deboparna Banik, Debapriya Krejcar, Ondrej Sarkar, Ram Kolkata700091 India University of Engineering and Management Kolkata700160 India Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Computer Science and Engineering RCC Institute of Information Technology Kolkata700015 India Department of Computer Science and Engineering ICFAI University Tripura Agartala799210 India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic

ISBN: (纸本)9789819726103

Cervical cancer is one of the most common causes of death among women worldwide. However, this fatal disease can be treated, and the mortality rate can be decreased if detected at an early stage. The Papanicolaou test is the gold standard for screening cervical cancer patients. However, the process of manual inspection is tedious and subject to manual errors. Thus, computer-based automatic screening is considered a viable alternative. To this end, in the present work, we have developed a novel system for classifying cervical cancer images as either normal or malignant. We have used the Herlev dataset in this study, which is the standard Pap smear image benchmark dataset. The proposed framework uses multiple base deep learning frameworks, namely Vision Transformer, Xception, VGG-19, VGG-16, and ResNet-101, for classification. Further, we have introduced a novel reward-based weighing technique to decide the weights of individual classifiers, which are, in turn, used to make decisions about the final class label of an input image using the weighted average technique. The proposed framework achieves an overall accuracy, precision, recall, F1-score, and AUC of 96%, 94%, 92%, 93%, and 94%, respectively, for the binary classification task that is normal vs. malignant. An elaborate study of the performance achieved by our proposed framework on the Herlev dataset shows it to be both robust and effective for the Pap smear image classification task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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