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Ontology-based Recommender System with Descriptive Analytics in e-Commerce 2

Ontology-based Recommender System with Descriptive Analytics...

引用

2nd International Conference on Big Data Engineering and Education, BDEE 2022

作者： Haw, Su-Cheng Chew, Lit-Jiew Ng, Kok-Why Naveen, Palanichamy Gandhi, Arfive Martha, Ati Suci Dian Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9781665481847

ISBN: (纸本)9781665481847

The amount of information and users has been increasing at a remarkable rate in recent years. This is where the recommender system comes in, recommender system is a system that generates a list of recommended products for the user. Recommender system has outshined as one of the important features in an e-Commerce portal. Several recommender techniques have been proposed, yet, problems such as cold-start item problem, cold-start user problem and data sparsity problem still existed. The expected outcomes of this paper are an ontology-based recommender system, combining the collaborative and content-based filtering approaches. In addition, the recommender system also generates recommendations by considering user preferences based on social network association. The experimental evaluation indicated that the proposed approach works more efficiently and effectively improves the recommendation. © 2022 IEEE.

关键词： Recommender systems

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Comparative analysis of Start-up Success Rate Prediction Using Machine Learning Techniques 3

Comparative analysis of Start-up Success Rate Prediction Usi...

引用

3rd International Conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Belgaum, Mohammad Riyaz Viswanath, Ediga Kasi Chakradhar, Pavukolla Goud, Ediga Chenna Kesava Chandu, Kuruva G. Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Kurnool India Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9798350391565

Software startups create innovative products or services using programming, which can lead to a need for products from potential customers. Researchers suggest that trial and error methodology can increase the success rate of these organizations by evaluating assumptions about client needs before fostering a product. However, programming new businesses typically do not require trial and error. The study aims to predict the success of startup companies in their early development stages by analyzing their success based on initial funding. A study of 106 unsuccessful software start-ups was conducted to predict the success rate. The experimentation progression model (X Pro) was developed to show that effective experimentation requires understanding trial and error, setting objectives, conducting tests, analyzing results, and acting based on new knowledge. The findings provide insights into supporting procedures and approaches that enhance trial- and-error reception in developing new business models. Researchers can become aware of potential factors leading to startup disappointment and take precautions to avoid them. The research focuses on using web-based open sources, including social networking services and the web, to model the task. The study uses machine learning techniques like Random Forest, Gradient Boost Classifier, XG Boost, and Ada Boost Classifier. The analysis provides insights into the types of useful signals discovered on the web and market mechanisms underlying the funding process. This approach offers a more comprehensive approach to predicting startup success. © 2024 IEEE.

关键词： Ada (programming language)

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Parkinson's Disease Induced Gain Freezing Detection using Gated Recurrent Units Optimized by Modified Crayfish Optimization Algorithm 5

Parkinson's Disease Induced Gain Freezing Detection using Ga...

引用

5th International Conference on Mobile computing and Sustainable informatics, ICMCSI 2024

作者： Bacanin, Nebojsa Petrovic, Aleksandar Jovanovic, Luka Zivkovic, Miodrag Zivkovic, Tamara Sarac, Marko Singidunum University Department of Informatics and Computing Belgrade Serbia Singidunum University Faculty of Technical Sciences Belgrade Serbia School of Electrical Engineering Belgrade Serbia

ISBN: (纸本)9798350395235

Parkinson's disease belongs to the group of health problems that are incurable but can be mitigated if treated properly. While there is no way of curing the damage caused by the disease, patient's life quality can be improved if diagnosed and treated properly and on time. The role of artificial intelligence (AI) in medicine is increasing. Deep learning algorithms may be utilized to automatically detect freezing of gait episodes. This study is focused on Parkinson's disease diagnosis based on the disturbances in the patient's gait which is affected by this disease. A hybrid deep and machine learning AI solution that employs the gated recurrent unit (GRU) neural network optimized by a hybrid swarm intelligence solution between the crayfish optimization algorithm and the firefly algorithm has been proposed. The proposed solution is compared to other high-performing optimization algorithms to establish objective grounds for comparison. The proposed framework results in the overall best performance and confirms the made improvements. The best-constructed model attained an accuracy of 87.08%. © 2024 IEEE.

关键词： Patient treatment

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Sarcasm Detection on Social Media Text Using Major Voting Ensemble Approach 4th

Sarcasm Detection on Social Media Text Using Major Voting En...

引用

4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Ibrahim, Muhammad Irham Sia, Florence Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS 88400 Sabah Kota Kinabalu Malaysia

ISBN: (纸本)9789819729760

With the increasing popularity of social media platforms such as Facebook, Instagram, and Twitter, people tend to use those platforms to share their ideas, reviews, and opinions through text. Their comments posted on social media platforms have become a critical source of feedback for many organizations on their programs and services. They may comment freely that occasionally transmit their opposing opinions implicitly through sarcastic text. This can cause the risk of misinterpreting the sarcastic comment which may lead to misunderstanding the context of the comment. Machine learning methods have been widely applied to automatically detect sarcasm in text which improves the analysis of sentiment in the text. The ensemble of these methods has showed the ability to detect sarcasm well compared to the single method. Majority voting is a simple yet widely used ensemble technique for improving classification performance. However, the effectiveness of the ensemble machine learning method based on majority voting has not yet been thoroughly explored in detecting sarcasm on social media text data. This paper proposes an ensemble machine learning model using majority voting for sarcasm detection on Twitter text data. Several experiments have been conducted to evaluate the accuracy performance of the majority voting ensemble-based methods to detect sarcasm in real world datasets consisting of Twitter text. The results show that the ensemble method using hard voting particularly outperformed the single methods in sarcasm detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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The NLP for Employee Review Sentiment Analysis: An Explainable Perspective 26

The NLP for Employee Review Sentiment Analysis: An Explainab...

引用

26th International Symposium on Symbolic and Numeric Algorithms for Scientific computing, SYNASC 2024

作者： Babic, Lepa Stoean, Ruxandra Cadjenovic, Jelena Jovanovic, Luka Zivkovic, Miodrag Bacanin, Nebojsa Singidunum University Department of Business Economics Belgrade Serbia University of Craiova Faculty of Sciences Craiova Romania Singidunum University Faculty of Technical Sciences Belgrade Serbia Singidunum University Faculty of Informatics and Computing Belgrade Serbia

ISBN: (纸本)9798331532833

Main goal of the research presented in this manuscript is to analyze employees satisfaction by employing hybrid methods between machine learning and metaheuristics. Furthermore, the proposed work utilizes term frequency-inverse document frequency (TF-IDF), natural language processing (NLP) method, for the identification of important segments of the employees' statements towards their workplaces, while the extreme gradient boosting (XGBoost) model is used for predicting the outcomes. Since each machine learning technique should be calibrated to particular problem, the XGBoost has been optimized by a modified swarm metaheuristics based on red fox optimizer (RFO) algorithm and its performance is evaluated against other high-performing swarm intelligence approaches. The results indicate the superiority of proposed solution, which has been further evaluated with the Shapley additive explanations (SHAP) analysis. © 2024 IEEE.

关键词： Swarm intelligence

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Towards Digital Twin Integrated Border Control

Towards Digital Twin Integrated Border Control

引用

2024 IEEE International Conference on computing, ICOCO 2024

作者： Tobing, Vianny Hamzah, Muzaffar Chew, Jackel Hamdan, Mosab Universiti Malaysia Sabah Faculty of Computing and Informatics Malaysia South East Technological University Walton Institute for Information and Communication System Science Waterford Ireland

ISBN: (纸本)9798331530303

As a promising enabling technology for digital transformation with the prime goal of solving numerous challenges, digital twin concept has gained significant recognition among researchers and practitioners, and has been adopted across many different industries. The importance of data to digital twin cannot be overstated. Data plays a critical role in constructing virtual models, building cyber-physical connections, and executing intelligent operations. Driven by data and model, the digital twin modeling is the core for accurate portrayal of its physical entity, which enables the digital twin to deliver a functional service in monitoring and prediction. Against this background, the paper suggests the adoption of digital twin within the border control domain for risk monitoring purposes. The monitoring function will identify, analyze, and respond to illegal activities at national entry points. The virtual data is collected from various sources of IoT devices. This research conducts a thorough review of existing literature and analysis on technology advancements to present the emerging trends and technology used in border control and subsequently proposed a model and framework of digital twin integrated border control that will support risk monitoring of border authority. Exploratory investigation was conducted in search for model and framework validation. Result of the survey shows that the majority of the respondents agreed with the idea of digital twin implementation within border control domain to support risk monitoring. The preliminary study proved that digital twin integration contributes to the effectiveness and efficiency of border protection routine. ©2024 IEEE.

关键词： Fog computing

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Cyberbully Detection by Using Machine Learning 4th

Cyberbully Detection by Using Machine Learning

引用

4th International Conference on Advances in Computational Science and Engineering, ICACSE 2023

作者： Tamring, Norazlinda Hung, Lai Po Creative Advanced Machine Intelligence Research Centre Faculty of Computing and Informatics Universiti Malaysia Sabah Jalan UMS Kota Kinabalu Sabah88400 Malaysia

ISBN: (纸本)9789819729760

Cyberbullying is a growing problem that affects mental health and academic achievement, especially among teenagers and young people. Detecting cyberbullying is challenging due to its complex and subjective nature, as well as the rapidly evolving technology and language. Victims have difficulty avoiding attacks, which can be as simple as malicious comments online. Effective models for detecting cyberbullying are needed to prevent such cases, but data extraction from social media is difficult due to privacy concerns and limited user information. The objective of this project is to enhance manual monitoring for cyberbullying on social networks and online platforms by researching and testing the most effective feature extraction method to be implemented. The project aims to achieve the following goals: preprocess the public dataset, design machine learning algorithm models with different feature extraction methods, evaluate the accuracy and performance of each algorithm, and create a simple interface to test sentences with the designed model. The classifiers used in the research are Support Vector Machine (SVM), Naive Bayes (NB) and Decision Tree (DT) that are optimized with two different feature extraction (TFIDF and Count Vectoriser). The classifier's evaluation is determined by accuracy, precision, recall, and f1-score. The classifier that obtained the highest performance is Linear SVM by using TFIDF. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Phishing

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A Novel Framework for Preserving Patient Health Record in IoT Healthcare 3

A Novel Framework for Preserving Patient Health Record in Io...

引用

3rd International Conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2024

作者： Belgaum, Mohammad Riyaz Pothureddy, Priyanka Patnam, Sai Suraksha Gadda, Parimala Deepthi G. Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Kurnool India Multimedia University Faculty of Computing and Informatics Cyberjaya Malaysia

ISBN: (纸本)9798350391565

As artificial intelligence, the Internet of Things (IoT), and next-generation mobile communication continue to evolve at a rapid pace, an increasingly popular application called the Internet of Medical Things (IoT) has arisen to provide ease and practicality in healthcare. Nonetheless, privacy preservation issues are brought up by accessing patient medical information inside the IoT framework, which calls for the creation of safe and private-preserving solutions. To address these issues, we provide in this study an IoT-Framework for preserving patient health record (FFPPHR) based on ELGamal blind signatures. We provide an enhanced privacy protection medical record searching technique (FFPPHR) based on the elliptic curve discrete logarithm problem (ECDLP) and point out security vulnerabilities in the current FFPPHR. Our security research shows that by utilizing the hard problem assumption of ECDLP, the FPMR guarantees identity privacy and accuracy. Furthermore, the use of Blind ECDSA Signatures improves data security. Performance assessments confirm the FFPPHR efficiency, and theoretical findings agree with simulations. The suggested FFPPHR approach yields lower time costs when compared to state-of-the-art techniques, which makes it a potential option for safe and effective medical record searches in IoT contexts. © 2024 IEEE.

关键词： Electronic health record

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Hand Sign Detection and Voice Conversion for the Hearing and Speech Impaired Using Convolutional Neural Networks 4th

Hand Sign Detection and Voice Conversion for the Hearing and...

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Belgaum, Mohammad Riyaz Sowmya, Kurni Sireesha, Kuruva Priyanka, Tatagari Tony Vyshnavi, Uppara Uravakonda Department of Computer Science and Engineering G. Pullaiah College of Engineering and Technology Kurnool India Faculty of Computing and Informatics Multimedia University Cyberjaya Malaysia

ISBN: (纸本)9789819994410

Sign Language Recognition (SLR) tries to convert sign language into written or spoken form to enable communication between a deaf-mute person and a normal person. The task of communicating with disabled and impaired persons is extremely challenging, owing to its complexity and variety of hand motions that have a major societal impact. In order to depict sign language motions, SLR approaches are increasingly being used. These classification models are based on hand-crafted characteristics. Building reliable features that can adapt to the diverse range of hand signs is nonetheless challenging. To address this issue, the authors proposed a special 3D convolutional neural network (CNN) to ease the communication between normal and mute communities. Our proposed model eliminates the requirement to create features by automatically extracting discriminative spatial–temporal features from the actual livestream without any prior information. To improve performance, the 3D CNN is fed with multiple channels of video feeds that include colour, depth, trajectory information, joint body locations, and depth clues. We illustrate the proposed model's superiority to the conventional methods based on hand-crafted characteristics by testing them against a real dataset collected using a depth camera. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Audition

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Evaluating the Utilization of Grammatical Structures of Arabic Language in Hiding Bits

Evaluating the Utilization of Grammatical Structures of Arab...

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2024 Advances in Science and Engineering Technology International Conferences, ASET 2024

作者： Rihan, Yazen Altaweel, Ala Khattab, Khattab Kamel, Ibrahim University of Sharjah College of Computing and Informatics Department of Computer Engineering Sharjah United Arab Emirates Ondokuz Mayis University Faculty of Ilahiyat Samsun Turkey

ISBN: (纸本)9798350344134

Nowadays, there is massive volume of data transmitted constantly through digital mediums. This data faces the risks of various cyber-attacks like eavesdropping, traffic analysis, packet injection, and packet dropping. There has been several security mechanisms to secure sensitive data and ensure their confidentiality, integrity, and availability. Indeed, Encryption, which is the process of converting the plain-text data into a non-understandable form known as the cipher-text, can secure the sensitive data. However, Encryption techniques are costly in terms of computational resources. Steganography, on the other hand, attempts to exploit the redundancy in data representation and structure to hide secret information within the data. Text steganography refers to the art of hiding secret information inside long passages of text. This paper proposes a new text steganography technique that exploits grammatical structures of Arabic language. Specifically, the paper exploits the redundancy in the language structure of Arabic grammar. The paper also presents the results of applying the proposed technique on a large dataset of articles from different categories such as politics, sports, and religion. The conducted experiments measure the number of secret bits that can be hidden in 1000 words for each category. The results show that some steganography rules can perform differently when annlied to different categories of text. © 2024 IEEE.

关键词： Data integrity

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