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Advancements in computing (ICAC), International Conference on

作者： H.M.A.P. Bandara J. Charles L. S. Lekamge Department of Computing and Information Systems Sabaragamuwa University of Sri Lanka Department of Physical Sciences and Technology Faculty of Applied Sciences

ISBN: (纸本)9798350398106

The rapid proliferation of internet-based technology has made the sharing economy the next e-commerce business model. Recently, sharing economy lodging platforms have gained a significant market share in the tourism and lodging industry. Tourism and hospitality industries are now being significantly disrupted by Airbnb, an online lodging platform. For businesses and customers who utilize these accommodation platforms, online reviews serve as quality indicators, affecting their decisions to make a transaction. Sentiment analysis and text mining can be used to analyze these online reviews to identify various factors embedded in them that can influence how guests perceive lodging in the sharing economy. Peer-to-peer accommodation platforms can benefit from analyzing these aspects since they can utilize the results to streamline their operations and give customers better services. Current research on this domain has only identified a limited number of important factors, such as trust, quality, security, price, cleanliness, and indoor environmental quality. However, there can be many other factors that can affect the accommodation experience. These factors would require further attention. Therefore, in this study a dataset pertaining to the Airbnb platform was considered which contained a total of 401 964 review comments. Word cloud, frequency distribution, and topic modeling were used as data analysis techniques to identify various factors affecting accommodation experience. Results indicate that factors including location, safety, host-guest interaction, amenities, proximity to restaurants and transit options, and apartment uniqueness can be primarily taken into account to give superior services to their clients.

关键词： Industries Text mining Sentiment analysis Analytical models Atmospheric modeling Sharing economy Tag clouds

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Mobile commerce and its apps - Opportunities and threats in Malaysia 2

Mobile commerce and its apps - Opportunities and threats in ...

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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Baker El-Ebiary, Yousef A. Aseh, Khairi Bamansoor, Samer Pande, Bishwajeet Abu-Ulbeh, Waheeb Yusoff, M. Hafiz Pathmanathan, P. Ravindran Jusoh, Julaily Aida Al Moaiad, Yazeed UniSZA Faculty of Informatics and Computing Malaysia International Institute of Applied Science Swiss School of Management Netherlands MEDIU Faculty of Computer and Information Technology Malaysia Gyancity Research Consultancy Pvt Ltd Motihari India Faculty of Computer Science and Informatics Cybersecurity Department Jordan Albukhary International University School of Computing and Informatic Kedah Malaysia Faculty of Informatics and Computing UniSZA Malaysia

ISBN: (纸本)9781665432221

The purpose of this study is to discuss on opportunities and threats of Foodpanda as an online E-commerce platform including their strength and weaknesses. Electronic Commerce commonly known as E - Commerce means buying and selling of goods and services through internet. E-commerce is a way of conducting business online. Though e-commerce is facing challenges in the market but it has scope to outsource the traditional markets. Nowadays, E-commerce is a very big and potential platform in business organization. There are so many platform of E-commerce and one of them is Foodpanda. Foodpanda is one of the leading online food delivery marketplaces and is spread out globally. Its main job is to enable users to place orders at nearby restaurants with the assistance of its website or via its mobile app. A study on Foodpanda will show on opportunities and threads of Foodpanda as one of the biggest online E-Commerce platform. © 2021 IEEE.

关键词： Mobile commerce

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Optical Character Recognition using Backpropagation Neural Network for Handwritten Digit Characters 7

Optical Character Recognition using Backpropagation Neural N...

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7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021

作者： Yap, Mei Ing Moorthy, Kohbalan Daud, Kauthar Mohd Ernawan, Ferda Universiti Malaysia Pahang Faculty of Computing College of Computing and Applied Sciences Pekan26600 Malaysia Universiti Kebangsaan Malaysia Center for Artificial Intelligence Technology Fakulti Teknologi Dan Sains Maklumat Bangi43600 Malaysia

ISBN: (纸本)9781665414074

Recognizing handwritten characters, the accuracy of the optical character recognition is usually not relatively high due to every person having their unique way of writing characters. Therefore, we focus on finding a high recognition accuracy of optical character recognition by using a backpropagation neural network. The input layer of the backpropagation neural network is the pixel number of the one-character image, which is 784 input nodes that will be the input layer of the neural network. Then the output layer of the neural network will be the 10-digit characters which are 0 to 9. The dataset that used for this research has a total of 280, 000 data. The output of the neural network will a computerized digit representing the recognized digit characters. The performance measurement is the recognition accuracy where the recognized data and the expected output data are compared and calculated. Additionally, the dataset was applied with salt and pepper noise to represent the corrupted data and use a median filter to repair the image. The recognition accuracy for the corrupted image and the corrected image are obtained and discussed. © 2021 IEEE.

关键词： Salt and pepper noise

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Automatic recognition of depression based on audio and video:A review

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World Journal of Psychiatry 2024年第2期14卷 225-233页

作者： Meng-Meng Han Xing-Yun Li Xin-Yu Yi Yun-Shao Zheng Wei-Li Xia Ya-Fei Liu Qing-Xiang Wang Shandong Mental Health Center Shandong UniversityJinan 250014Shandong ProvinceChina Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and TechnologyQilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250353Shandong ProvinceChina Department of Ward Two Shandong Mental Health CenterShandong UniversityJinan 250014Shandong ProvinceChina

Depression is a common mental health *** current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression ***-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively *** physicians usually require extensive training and experience to capture changes in these *** in deep learning technology have provided technical support for capturing non-biological *** researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression *** article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.

关键词： Depression recognition Deep learning Automatic depression estimation System Audio processing Image processing Feature fusion Future development

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Analysis of Fraudulent Job Postings Using Machine Learning 5

Analysis of Fraudulent Job Postings Using Machine Learning

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5th International Conference on Intelligent computing, Communication, Networking and Services, ICCNS 2024

作者： Salloum, Said Tahat, Khalaf Mansoori, Ahmed Alfaisal, Raghad Tahat, Dina School of Science Engineering and Environment University of Salford Manchester United Kingdom United Arab Emirates University and Yarmouk University Media and Creative Industries Jordan United Arab Emirates University Media and Creative Industries Department United Arab Emirates Universiti Pendidikan Sultan Idris Faculty of Computing and Meta-Technology Tanjung Malim Malaysia Al Ain University Applied Sociology Department United Arab Emirates

ISBN: (纸本)9798350354690

In the age of digital recruitment, the proliferation of fraudulent job postings poses significant challenges for job seekers and legitimate employers alike. These deceptive listings not only waste time and resources but also endanger personal data and propagate scams. Addressing this issue, we present a comprehensive machine learning methodology to accurately discern between genuine and counterfeit job opportunities. Leveraging a rich dataset procured from Kaggle, this paper details the deployment of a logistic regression classifier, judiciously trained on a fusion of textual and meta-features extracted from job advertisements. The classifier underwent rigorous evaluation, manifesting an impressive accuracy of 96.78% in segregating authentic posts from fraudulent ones. The implementation of Term Frequency-Inverse Document Frequency (TF-IDF) vectorization on textual data, alongside meta-features such as job description length, enabled the model to learn and predict with high precision. The implications of this research are substantial, offering a scalable and efficient tool for job platforms to safeguard their users and ensure the integrity of their listings. © 2024 IEEE.

关键词： Logistic regression

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Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

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Intelligent Automation & Soft computing 2023年第8期37卷 2499-2517页

作者： Ammar Almomani Iman Akour Ahmed M.Manasrah Omar Almomani Mohammad Alauthman Esra’a Abdullah Amaal Al Shwait Razan Al Sharaa School of Computing Skyline University CollegeUniversity City of SharjahP.O.Box 1797SharjahUnited Arab Emirates IT-Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan Information Systems Department College of Computing&InformaticsUniversity of SharjahUnited Arab Emirates Comp.Info Sciences(CIS)Division Higher Colleges of TechnologySharjahUnited Arab Emirates Computer Sciences Department Yarmouk UniversityIrbidJordan Computer Network and Information Systems Department The World Islamic Sciences and Education UniversityAmman11947Jordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan

The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network ***,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion *** vast network traffic volume also complicates most network monitoring systems and *** intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these *** study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-Nearest-Neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning *** stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-Nearest-Neighbors)and a meta-model represented by the Logistic Regression *** using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,*** obtained results demonstrate improvements in other measurement criteria.

关键词： Intrusion detection system(IDS) machine learning techniques stacking ensemble random forest decision tree k-nearest-neighbor

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Improvement of Swarm Intelligence Algorithm and Its Application in Logistics Network Routing

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1077-1094页

作者： Zhang, Xin Wei, Yanqiu Hashim, Zrdplwa School of Business Applied Technology College of Soochow University Kunshan 215325 China Fan Li Business School Shaoxing Vocational Technical College Shaoxing 312000 China Faculty of Engineering Computing Science Swinburne University of Technology Kuching93350 Malaysia

The Vehicle Routing Problem (VRP) is a key aspect of logistics network routing, and an excellent routing optimization strategy can effectively improve the service experience of users and reduce transportation costs. With the explosive growth of e-commerce, the logistics network routing system is faced with a huge number of demand points and customer-specified receiving time slots, which poses a huge challenge for routing optimization of large-scale delivery. To address the problems of high total cost and low effective vehicle utilisation in the logistics network routing system, this work introduces a new swarm intelligence method, the pigeon-inspired optimization (PIO), and improves it. Two improvement strategies are proposed to address the strengths and weaknesses of the PIO algorithm. Firstly, by combining the high swarm dispersion of the quantum evolutionary algorithm and the fast convergence of the PIO, the PIO method is upgraded by mixing the algorithms to achieve the effect of complementing each other’s strengths and improving the global exploration ability of the PIO algorithm;secondly, a Gaussian variation operator is added to the PIO algorithm to enhance its local exploitation capa-bility and prevent prematurity in order to retain the variety in future iterations. Each individual contains information on both client points and routes. The effectiveness of the improved pigeon flock intelligence optimisation algorithm is verified through test function simulations. The effectiveness and rationality of the improved PIO algorithm is verified in a case study based on the Solmon arithmetic example, which has some engineering application value. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Routing algorithms

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Leveraging K-Means Clustering for Analysis of Arabic Hate Speech Tweets

Leveraging K-Means Clustering for Analysis of Arabic Hate Sp...

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2024 Global Congress on Emerging Technologies, GCET 2024

作者： Salloum, Said Tahat, Khalaf Mansoori, Ahmed Alfaisal, Raghad Tahat, Dina University of Salford School of Science Engineering and Environment Manchester United Kingdom United Arab Emirates University Yarmouk University Media & Creative Industries Jordan United Arab Emirates University Media & Creative Industries Department United Arab Emirates Universiti Pendidikan Sultan Idris Faculty of Computing and Meta-Technology Tanjung Malim Malaysia Al Ain University Applied Sociology Department United Arab Emirates

ISBN: (纸本)9798331542603

As hate speech is becoming common on social media platforms, it is important to detect, and curb hate speech in order to provide a better and safe environment online. Given the heavy usage of manual methods of hate speech detection, researchers started putting efforts in the direction of machine-learning-based automated methods sooner or later. Many available datasets and models on hate speech detection are largely inadequate for Arab hate speech because of the complexity of language and cultural nuances. In this paper, the researchers eased these difficulties, as they, used the K-Means to apply compilation for Arab hate speech in the L-HSAB dataset on the tweets. Methodology consisted of Prospecting and Pre-processing, Term Frequency Inverse Document frequency (TF-IDF) dimension reductions, through Principal Component Analysis (PCA), and assembly via K-Means. They helped to identify different sets of hate speech tweets and understand the common topics and topics. It has led to new understandings of how Arab hate speech flows on the Internet and now offers the potential for tailored interventions. Automated hate speech analysis via machine learning would allow policymakers to formulate tailored modification strategies focused on making the Internet safer and the community more harmonious. © 2024 IEEE.

关键词： Tweets

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Encryption with Image Steganography Based Data Hiding Technique in IIoT Environment

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Computers, Materials & Continua 2022年第7期72卷 1323-1338页

作者： Mahmoud Ragab Samah Alshehri Hani A.Alhadrami Faris Kateb Ehab Bahaudien Ashary SAbdel-khalek Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah21589Saudi Arabia Molecular Diagnostic Lab King Abdulaziz University HospitalKing Abdulaziz UniversityJeddah21589Saudi Arabia Special Infectious Agent Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohag82524Egypt

Rapid advancements of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)pose serious security issues by revealing secret ***,security data becomes a crucial issue in IIoT communication where secrecy needs to be guaranteed in real ***,AI techniques can be utilized to design image steganographic techniques in *** addition,encryption techniques act as an important role to save the actual information generated from the IIoT devices to avoid unauthorized *** order to accomplish secure data transmission in IIoT environment,this study presents novel encryption with image steganography based data hiding technique(EISDHT)for IIoT *** proposed EIS-DHT technique involves a new quantum black widow optimization(QBWO)to competently choose the pixel values for hiding secrete data in the cover *** addition,the multi-level discrete wavelet transform(DWT)based transformation process takes ***,the secret image is divided into three R,G,and B bands which are then individually encrypted using Blowfish,Twofish,and Lorenz Hyperchaotic *** last,the stego image gets generated by placing the encrypted images into the optimum pixel locations of the cover *** order to validate the enhanced data hiding performance of the EIS-DHT technique,a set of simulation analyses take place and the results are inspected interms of different *** experimental outcomes stated the supremacy of the EIS-DHT technique over the other existing techniques and ensure maximum security.

关键词： IIoT security data hiding technique image steganography encryption secure communication

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Universal Scaling Bounds on a Quantum Heat Current

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Physical Review Letters 2023年第9期131卷 090401-090401页

作者： Shunsuke Kamimura Kyo Yoshida Yasuhiro Tokura Yuichiro Matsuzaki Faculty of Pure and Applied Sciences University of Tsukuba Tsukuba 305-8571 Japan Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan

In this Letter, we derive new bounds on a heat current flowing into a quantum L-particle system coupled with a Markovian environment. By assuming that a system Hamiltonian and a system-environment interaction Hamiltonian are extensive in L, we prove that the absolute value of the heat current scales at most as Θ(L3) in a limit of large L. Furthermore, we present an example of noninteracting particles globally coupled with a thermal bath, for which this bound is saturated in terms of scaling. However, the construction of such a system requires many-body interactions induced by the environment, which may be difficult to realize with the existing technology. To consider more feasible cases, we consider a class of the system where any nondiagonal elements of the noise operator (derived from the system-environment interaction Hamiltonian) become zero in the system energy basis, if the energy difference exceeds a certain value ΔE. Then, for ΔE=Θ(L0), we derive another scaling bound Θ(L2) on the absolute value of the heat current, and the so-called superradiance belongs to a class for which this bound is saturated. Our results are useful for evaluating the best achievable performance of quantum-enhanced thermodynamic devices, including far-reaching applications such as quantum heat engines, quantum refrigerators, and quantum batteries.

关键词： Open quantum systems & decoherence Quantum control

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