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Product Recommendation Using Collaborative Filtering and K-Means Clustering

Product Recommendation Using Collaborative Filtering and K-M...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： M Ranjith Kumar S Vishnu G Roshen D Niranjan Kumar P Revathi David Ricardo Ledo Baster Department of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Dept. of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Dept. of Computer Science & Engineering C. Abdul Hakeem College of Engineering and Technology Melvisharam Vellore India Dept. of Informatics and Mathematics University of Holguin Cuba

The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today’s world, e-commerce websites are transitioning from traditional search-driven methods to customized and intuitive approaches via product suggestions. Product recommendation systems are vital in e-commerce, from bringing new business to retaining existing ones. Our three-part recommendation system is designed so that new users have a great and engaging experience as the Product Popularity -Based System shows them carefully chosen products that are in demand. Collaborative Filtering Recommendations are highly personalized recommendations given to people who have already made their first purchases based on their prior actions and preferences. The K-Means Clustering-Based Recommendation System uses textual clustering analysis to deliver contextually relevant recommendations. We use a variety of evaluation metrics, such as click-through rates, user engagement, and the Silhouette Score, to assess the effectiveness and accuracy of our recommendation systems. Our findings show significant increases in user engagement, conversion rates, and relevant recommendations. Our findings demonstrate the transformative power of well-designed recommendation systems, which improve user experiences and retention and provide invaluable solutions for businesses entering the e-commerce space. This paper provides an in-dept. examination of the multifaceted landscape of e-commerce recommendations, shedding light on their far-reaching implications for customer acquisition and retention in this dynamic digital era.

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Dheya Mustafa Safaa M. Khabour Ahmed S. Shatnawi Eyad Taqieddin Dept. of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan Dept. of Information Systems Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan Dept. of Software Engineering Jordan University of Science & Technology Irbid Jordan Dept. of Cyber Security School of Computing and Informatics Al-Hussein Technical University Amman Jordan

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier.

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Quality Grading of Beef using Deep Convolutional Neural Network 9

Quality Grading of Beef using Deep Convolutional Neural Netw...

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9th International Conference on Information technology Trends, ITT 2023

作者： Sharma, Aarav Choudhury, Tanupriya Sarkar, Tanmay Mohanty, Madhusmita Bansal, Nikunj Mohanty, Sachi Nandan Informatics Cluster School of Computer Science Uttarakhand Dehradun2480007 India Cse Dept. Uttarakhand Dehradun India Graphic Era Hill University Cse Dept. Uttarakhand Dehradun248002 India Malda Polytechnic West Bengal State Council of Technical Education Govt. of West Bengal Department of Food Processing Technology Malda732102 India Andhra Pradesh Amaravati India VIT-AP University School of Computer Science & Engineering Andhra Pradesh Amaravati5222237 India

ISBN: (纸本)9798350327502

Beef is one of the most widely consumed meat, being an organic substance it is prone to degradation over time. In our paper we have proposed a Convolutional Neural Network model to grade a given sample of Beef and predict its freshness as good or bad. We have proposed a CNN model architecture along with a data (image) pre-processing pipeline that can be used to classify a given Beef sample. We have done comparative analysis between different pre-processing pipelines as well as other CNN based classification models and after training have achieved 99.63% test accuracy. © 2023 IEEE.

关键词： computer vision

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A Framework for Digital Twin Collaboration 24

A Framework for Digital Twin Collaboration

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Proceedings of the Winter Simulation Conference

作者： Zhengchang Hua Karim Djemame Nikos Tziritas Georgios Theodoropoulos Dept. of Computer Science and Eng. Southern University of Science and Technology (SUSTech) Shenzhen CHINA and School of Computing University of Leeds Leeds UK School of Computing University of Leeds Leeds UK Dept. of Informatics and Telecommunications University of Thessaly Lamia GREECE Research Institute of Trustworthy Autonomous Systems (RITAS) SUSTech Shenzhen CHINA and Dept. of Computer Science and Eng. Southern University of Science and Technology (SUSTech) Shenzhen CHINA

ISBN: (纸本)9798331534202

Digital Twins (DTs) have emerged as a powerful tool for modeling Large Complex Systems (LCSs). Their strength lies in the detailed virtual models that enable accurate predictions, presenting challenges in traditionally centralized approaches due to the immense scale and decentralized ownership of LCSs. This paper proposes a framework that leverages the prevalence of individual DTs within LCSs. By facilitating the exchange of decisions and predictions, this framework fosters collaboration among autonomous DTs, enhancing performance. Additionally, a trust-based mechanism is introduced to improve system robustness against poor decision-making within the collaborative network. The framework's effectiveness is demonstrated in a virtual power plant (VPP) scenario. The evaluation results confirm the system's objectives across various test cases and show scalability for large deployments.

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Classifying Emotions in Roman Urdu Posts using Machine Learning 1

Classifying Emotions in Roman Urdu Posts using Machine Learn...

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1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

作者： Nabeel, Zahra Mehmood, Maseeha Baqir, Anees Amjad, Anam Dept. of Software Engineering Sialkot Pakistan Ca'Foscari University of Venice Dept. of Environmental Sciences Informatics and Statistics Venice Italy National University of Sciences and Technology Dept. of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665424134

Emotion, a state of human mind has a significant impact on human behavior, social interactions and decision making. Few emotions from Carroll E. Izard model such as long-term fear, anger and sadness cause mental disorders which can lead to deterioration of mental and physical health. For the well-being of mental health, different psychological parameters such as voice, facial expressions and body movements were used to extract these emotions in earlier times. This paper focuses on verbal language for emotion classification. With the huge popularity of Web 3.0, people feel more comfortable in sharing their emotions over social media due to the anonymity of the web. Recently, researchers around the globe are interested to classify the emotions from social media posts (i.e., tweets, comments or micro-blogs etc.) using resource-rich languages e.g., English. However, less attention is paid towards the resource-poor language such as Urdu in Roman script. According to the Gallup survey in Pakistan, Urdu in Roman script is widely used to transmit text. To detect emotions in Roman Urdu text is a big challenge due to its scripting and morphology. In this paper, the following contributions are made;1) A corpus of Roman Urdu is developed for emotion classification. 2) We aim to identify user's mental states using textual posts on social media plat-forms;Instagram, Facebook, Twitter and YouTube. Particularly, Natural Language Processing methods incorporated with feature extraction methods (TF-IDF) and four supervised classifiers such as Support Vector Machine (SVM),Naïve Bayes, Random Forest and K-nearest Neighbor are used. By utilizing 80% training and 20% testing data, Naive Bayes secures 70.57% accuracy. 3) Moreover, suitability analysis is performed to identify the most suitable classifier in terms of accuracy, training and testing time. Results show that Naive Bayes is the most suitable classifier for emotion classification. © 2021 IEEE.

关键词： Deterioration

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Selecting Machine Learning Models for Student Performance Prediction Aligned with Pedagogical Objectives

Selecting Machine Learning Models for Student Performance Pr...

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Computing (ICOCO), IEEE International Conference on

作者： Ijaz Khan Mohammed Hazli Mohamed Zabil Abdul Rahim Ahmad Nafaa Jabeur Department of Computing College of Computing and Informatics Univsiti Tenaga Nasional Kajang Malaysia Information Technology Dept. Buraimi University College Al-Buraimi Oman Deparment of Computing and Informatics' Universiti Tenaga Nasional (UniTen) Kajang Malaysia Computer Science Dept. German University of Technology Muscat Oman

Machine learning classifiers emerge as productive tools to develop prediction models which forecast the final outcome of the students, in a course, and provide an opportunity to the instructor to take appropriate measures. A single prediction model may not be enough to achieve all the objectives of an instructor. The selection of appropriate prediction models is yet a challenging task. This paper proposes a novel framework that applies a set of machine learning classifiers over the training dataset of a course. Along with the training dataset, the instructor clarifies the prime of objective of the binary model whether it must focus over the identification of fail, pass, or both students. The framework recommends a convenient model to achieve the specific objectives of the instructor. This research concludes that models inclined to reduce misclassification of minority class are suitable if the primary objective of the institution is the correct identification of students who are struggling to achieve the minimum course requirements. Further, a model specialized solely in amplifying the correct classification of majority class instance is appropriate if the aim is to focus on the correct identification of excellent students. The empirical analysis in this research leads towards the fact that accuracy alone is not an adequate metric to assess the performance of a model and that systematic selection of evaluation metrics is required to develop constructive models.

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Towards the Selection of Distance Metrics for k-NN Classifier in Students' Performance Prediction Modeling

Towards the Selection of Distance Metrics for k-NN Classifie...

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Computing (ICOCO), IEEE International Conference on

作者： Ijaz Khan Mohammed Hazli Mohamed Zabil Abdul Rahim Ahmad Nafaa Jabeur Department of Computing College of Computing and Informatics' Univsiti Tenaga Nasional Kajang Malaysia Information Technology Dept. Buraimi University College Al-Buraimi Oman Deparmtne of Computing and Informatics' Universiti Tenaga Nasional (UniTen) Kajang Malaysia Computer Science Dept. German University of Technology Muscat Oman

This paper investigates the impact of changing distance metrics on the performance of the k-NN classifier. The study investigates the variation in models performance with changing distance metric and value of k in the context of students' performance prediction models. The research utilizes datasets specifically designed for students' performance prediction modeling. Starting with a I-NN model, the experiments increment the value of k by 2 until the size of the dataset is reached. The experiments are repeated with different distance metrics derived from Minkowski derivation, including Euclidean, Manhattan, and Chebyshev. The findings indicate that there is no unanimously dominant distance metric for every dataset. However, the Euclidean and Manhattan distance metrics emerge effective, while Chebyshev exhibits lower performance. The research concludes Euclidean and Manhattan distance metrics as appropriate metrics for students' performance prediction modeling using the k-NN classifier.

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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study

The Correlation Between Architecture Smells, Package Stabili...

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Sciences and Techniques of Automatic Control and computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mrwan BenIdris Ehab A. Omar Elfallah Marwa Aslam Cheema Mohammed Salah Addin Alsma Tareq Ziad Kadiki Abdelfattah Elbarsha Dep. of Computer Science Faculty of Information Technology University of Benghazi Dept. of Software Engineering Faculty of Information Technology Libyan International Medical University Health Informatics Department Faculty of Public Health University of Benghazi

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-dept. research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic Dependency and Unstable Dependency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable Dependency and reusability. Finally, we found a slight negative correlation between package stability and reusability.

关键词： Correlation coefficient Correlation Codes Conferences computer architecture Software systems Software development management

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An Investigation of Running Load Comparisons of ARCore on Native Android and Unity for Outdoor Navigation System Using Smartphone

An Investigation of Running Load Comparisons of ARCore on Na...

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Vocational Education and Electrical Engineering, International Conference on

作者： Komang Candra Brata Nobuo Funabiki Sritrusta Sukaridhoto Evianita Dewi Fajrianti Mustika Mentari Dept. of Information and Communication Systems Okayama University Okayama Japan Dept. of Informatics Engineering Universitas Brawijaya Malang Indonesia Dept. of Informatic and Computer Politeknik Elektronika Negeri Surabaya Surabaya Indonesia Dept. of Information Technology State Polytechnic of Malang Malang Indonesia

In recent years, the utilization of location-based augmented reality (LAR) has gained popularity across various domains, including tourism, education, gaming, and advertising. The advancements in AR technology have made it easier to develop immersive LAR applications. ARCore, a widely adopted AR development library, enables the creation of location-based AR apps on Android devices. However, the implementation of ARCore in real-time scenarios that demand significant computational resources, like outdoor pedestrian navigation, can vary depending on the chosen development environment. This research aims to explore and compare the running load performance of ARCore implementation in location-based AR applications developed with Unity and Android Native, which are two prevalent AR development platforms. The study involves building a prototype LAR application using both Unity and native Android, followed by comprehensive running load performance evaluations. The findings reveal that while both Unity and native Android apps can deliver compelling location-based AR experiences, native Android apps demonstrate superior performance and resource efficiency in outdoor scenarios, whereas Unity offers the advantage of multi-platform support and ease of development for complex 3D assets. These insights provide valuable guidance to developers in selecting the most suitable approach for developing location-based AR applications for navigation, aligning with their specific needs and requirements.

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Atherosclerotic Carotid Plaque AM-FM Texture Feature Variability in Ultrasound Video

Atherosclerotic Carotid Plaque AM-FM Texture Feature Variabi...

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IEEE International Symposium on Biomedical Imaging

作者： Kyriacos P. Constantinou Ioannis P. Constantinou Christos P. Loizou Efthyvoulos Kyriacou Andreas Panayides Andrew N. Nicolaides Constantinos S. Pattichis Marios S. Pattichis Department of Computer Science University of Cyprus Nicosia Cyprus Istognosis Ltd Nicosia Cyprus Dept. of Electrical Computer Engineering and Informatics Cyprus University of Technology Cyprus CYENS Centre of Excellence Nicosia Cyprus Cyprus Cardiovascular Disease Educational Research Trust Nicosia Cyprus Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The objective of this work was to investigate the Amplitude Modulation - Frequency Modulation (AM-FM) texture feature variability in carotid ultrasound video during the cardiac cycle at systole and diastole. The goal here was to identify AM-FM features that are associated with increased risk of stroke. We computed the instantaneous amplitude, instantaneous phase and the magnitude of instantaneous frequency to extract plaque histogram features. A small dataset of 5 asymptomatic and 3 symptomatic videos were analyzed. Selected AM-FM plaque histogram texture features extracted during the cardiac cycle at the systolic and diastolic states were statistically significantly different between asymptomatic and symptomatic videos. However, further evaluation with more subjects needs to be carried out to exploit the usefulness of the proposed analysis in the clinical context.

关键词： Histograms Ultrasonic imaging Frequency modulation Stability criteria Atherosclerosis Feature extraction Object recognition

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