检索结果-内蒙古大学图书馆

Revisiting recommender systems: an investigative survey

Neural Computing and Applications 2025年第4期37卷 2145-2173页

作者： Ibrahim, Osman Ali Sadek Younis, Eman M. G. Mohamed, Ebtsam A. Ismail, Walaa N. Computer Science Department Minia University Minia Egypt Information Systems Department Minia University Minia Egypt Management Information system Department Al-Yamamah University Riyadh Saudi Arabia

This paper provides a thorough review of recommendation methods from academic literature, offering a taxonomy that classifies recommender systems (RSs) into categories like collaborative filtering, content-based systems, and hybrid systems. It examines the effectiveness and challenges of these systems, such as filter bubbles, the "cold start" issue, and the reliance on collaborative filtering and content-based approaches. We trace the development of RSs, emphasizing the role of machine learning and deep learning models in overcoming these challenges and delivering more accurate, personalized, and context-aware recommendations. We also highlight the increasing significance of ethical considerations, including fairness, transparency, and trust, in the design of RSs. The paper presents a structured literature review, discussing various aspects of RSs, such as collaborative filtering, personalized recommender systems, and strategies to improve system robustness. It also points out the limitations of the existing approaches and suggests promising research directions for the future. In summary, this paper offers a comprehensive analysis of RSs, focusing on their evolution, challenges, and potential future improvements, particularly in enhancing accuracy, diversity, and ethical practices in recommendations. © The Author(s) 2025.

关键词： Collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Shopping Experiences with Smart Carts 1

Revolutionizing Shopping Experiences with Smart Carts

引用

1st IEEE International Conference on Computing and Data Science, ICCDS 2024

作者： Garlapati, Sasrika Reddy Dugad, Aastha Sachinkumar Agarwal, Shreya Gupta, Arul Seetha, R. School of Computer Science and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350365337

The IoT -based smart shopping cart system revolutionizes the retail experience by integrating RFID technology, image processing cameras, and weight sensors along with bill generation and payment checkout. RFID scanners on all four sides and image processing ensures product accuracy. A weight sensor verifies added products' weight, aligning with the bill generated on the customer's screen. The system displays real-time information, including product details, prices, discounts, and total bill. A robust database management system ensures data integrity and security. This innovative system transforms conventional shopping into a futuristic and efficient experience. Human intervention in shopping can be minimized. © 2024 IEEE.

关键词： Image processing

来源：评论

学校读者我要写书评

暂无评论

Enhancing IoT Security: Deep Learning Driven Web Phishing Attack Detection and Classification Model 4

Enhancing IoT Security: Deep Learning Driven Web Phishing At...

引用

4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Alabdan, Rana College of Computer and Information Sciences Majmaah University Department of Information systems Al Majmaah Saudi Arabia

ISBN: (纸本)9798350352931

The Internet of Things (IoT) is a technique that allows our day-to-day life objects to interconnect from the Internet and receive and send information for a meaningful intention. Nowadays, IoT has resulted in various revolts in nearly all areas of our societies. Phishing is the dominant threat to each internet user, in which invaders intend to falsely remove complex data from a system or user, with pretended websites, emails, and so on. Using the fast improvement in IoT gadgets, invaders are directing IoT devices like smart cars, security cameras, and so forth and performing phishing assaults to obtain control across such susceptible strategies for mischievous targets. This article focuses on the design of Al-Biruni earth radius optimization with Deep Learning for Web Phishing Attack Detection and Classification (BERDL-WPADC) technique. The presented BERDL-WPADC model majorly focuses on the identification and classification of web phishing attacks in the IoT environment. Primarily, the BERDL-WP ADC technique applies preprocesses the input data to convert it into a well-matched arrangement. Next, the classification of phishing web attacks occurs with a bi-directional LSTM (Bi-LSTM) classifier. For better the classification performance of the Bi-LSTM classifier, the parameter tuning process is performed through Al-Biruni earth radius optimization (BER) algorithm which supports attaining improved classification performance. The stimulated outcome study of the BERDL-WPADC model occurs and the outcomes are reviewed in terms of several aspects. The experimental investigation indicates the superiority of the BERDL-WPADC method across the existing methods. © 2024 IEEE.

关键词： Phishing

来源：评论

学校读者我要写书评

暂无评论

Exploring Emotional Cues Within Conversational Contexts for Sarcasm Detection 6

Exploring Emotional Cues Within Conversational Contexts for ...

引用

6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Hassan, Ahmed Helal, Nivin A. Afify, Yasmine M. Badr, Nagwa L. Ain Shams University Faculty of Computer and Information Sciences Information Systems Department Cairo11566 Egypt

ISBN: (纸本)9798350378511

Sarcasm, widely used in social media and everyday conversations, presents a substantial challenge for natural language understanding. Enhancing the ability to accurately interpret sarcasm is crucial for advancing natural language processing capabilities and enhancing communication comprehension. Traditional approaches to sarcasm detection often overlook the subtle emotional cues embedded within conversational contexts, which play a crucial role in its interpretation. In this paper, we investigate the impact of integrating emotions into contextual data and analyze how excluding neutral emotion influences sarcasm detection. This analysis aims to provide insights into factors that affect sarcasm comprehension. By using Mustard dataset, we employ the DistilBERT model to extract emotional features for each speaker in conversational exchanges. Subsequently, we use transformer architectures like BERT, DistilBERT, Electra, and RoBERTa in training and evaluating sarcasm detection. Our findings highlight the critical role of emotions in sarcasm interpretation, with RoBERTa achieving the highest accuracy of 0.937 and F1-score of 0.943, outperforming other models by up to 4.6% in F1-score. It also surpasses the existing work by 1.6% in F1-score when neutral emotion is excluded from the conversation context and by 4.3 % when neutral emotion is excluded from both the conversation context and the implicit emotions of the last utterance. © 2024 IEEE.

关键词： Context and Sentiment Analysis Emotion Sarcasm Detection Transformer Models

来源：评论

学校读者我要写书评

暂无评论

A Novel Human Interaction Framework Using Quadratic Discriminant Analysis with HMM

引用

computers, Materials & Continua 2023年第11期77卷 1557-1573页

作者： Tanvir Fatima Naik Bukht Naif Al Mudawi Saud S.Alotaibi Abdulwahab Alazeb Mohammed Alonazi Aisha Ahmed AlArfaj Ahmad Jalal Jaekwang Kim Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Information Systems Department Umm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Convergence Program for Social Innovation Sungkyunkwan UniversitySuwon03063Korea

Human-human interaction recognition is crucial in computer vision fields like surveillance,human-computer interaction,and social *** enhances systems’ability to interpret and respond to human behavior *** research focuses on recognizing human interaction behaviors using a static image,which is challenging due to the complexity of diverse *** overall purpose of this study is to develop a robust and accurate system for human interaction *** research presents a novel image-based human interaction recognition method using a Hidden Markov Model(HMM).The technique employs hue,saturation,and intensity(HSI)color transformation to enhance colors in video frames,making them more vibrant and visually appealing,especially in low-contrast or washed-out *** filters reduce noise and smooth imperfections followed by silhouette extraction using a statistical *** extraction uses the features from Accelerated Segment Test(FAST),Oriented FAST,and Rotated BRIEF(ORB)*** application of Quadratic Discriminant Analysis(QDA)for feature fusion and discrimination enables high-dimensional data to be effectively analyzed,thus further enhancing the classification *** ensures that the final features loaded into the HMM classifier accurately represent the relevant human *** impressive accuracy rates of 93%and 94.6%achieved in the BIT-Interaction and UT-Interaction datasets respectively,highlight the success and reliability of the proposed *** proposed approach addresses challenges in various domains by focusing on frame improvement,silhouette and feature extraction,feature fusion,and HMM *** enhances data quality,accuracy,adaptability,reliability,and reduction of errors.

关键词： Human interaction recognition HMM classification quadratic discriminant analysis dimensionality reduction

来源：评论

学校读者我要写书评

暂无评论

Identifying Fake Products using Blockchain Technology in Supply Chain System 11

Identifying Fake Products using Blockchain Technology in Sup...

引用

11th IEEE International Conference on Intelligent Computing and information systems, ICICIS 2023

作者： Medhat, Michael Wagih, Philip Mamdouh, Mario Ashraf, Kermina Farid, Michael Abo-Alian, Alshaimaa Ain Shams University Faculty of Computer and Information Sciences Department of Information Systems Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Information Systems Department Egypt

ISBN: (纸本)9798350322101

With the rise of globalization and the rapid advancements in technology, the volume of production has surged, making it easier to acquire counterfeit goods to an unprecedented degree. All types of industrial manufacturers and distributors are currently working to increase supply chain operations' openness to prevent counterfeiting, whether it be for food, medicine, or luxury goods. In order to identify counterfeit products within the supply chain system, this paper proposes a decentralized Blockchain-based application system (DApp) to authenticate products and detect counterfeit products. With the quick development of the Blockchain technology, It is widely acknowledged that the data stored there is secure and unchangeable. As a result, this idea is used in the proposed system to handle the transfer of ownership of goods. A Quick Response (QR) code issued by the DApp for each product connected to the Blockchain can be scanned by a customer to confirm the distribution and ownership information of the goods. © 2023 IEEE.

关键词： Blockchain

来源：评论

学校读者我要写书评

暂无评论

Hill Matrix and Radix-64 Bit Algorithm to Preserve Data Confidentiality

引用

computers, Materials & Continua 2023年第5期75卷 3065-3089页

作者： Ali Arshad Muhammad Nadeem Saman Riaz Syeda Wajiha Zahra Ashit Kumar Dutta Zaid Alzaid Rana Alabdan Badr Almutairi Sultan Almotairi Department of Computer Science National University of TechnologyIslamabadPakistan Department of Computing Abasyn UniversityIslamabadPakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia Department of Computer Science Faculty of Computer and Information SystemsIslamic university of MadinahMedinah42351Saudi Arabia Department of Information Systems Faculty of Computer and Information Sciences CollegeMajmaah University11952Saudi Arabia Department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah 11952Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data,but these techniques and algorithms cannot be used to protect data from an *** cryptography is the best way to transmit data in a secure and reliable *** researchers have developed various mechanisms to transfer data securely,which can convert data from readable to unreadable,but these algorithms are not sufficient to provide complete data *** algorithm has some data security *** some effective data protection techniques are used,the attacker will not be able to decipher the encrypted data,and even if the attacker tries to tamper with the data,the attacker will not have access to the original *** this paper,various data security techniques are developed,which can be used to protect the data from attackers ***,a customized American Standard Code for information Interchange(ASCII)table is *** value of each Index is defined in a customized ASCII *** an attacker tries to decrypt the data,the attacker always tries to apply the predefined ASCII table on the Ciphertext,which in a way,can be helpful for the attacker to decrypt the *** that,a radix 64-bit encryption mechanism is used,with the help of which the number of cipher data is doubled from the original *** the number of cipher values is double the original data,the attacker tries to decrypt each *** of getting the original data,the attacker gets such data that has no relation to the original *** that,a Hill Matrix algorithm is created,with the help of which a key is generated that is used in the exact plain text for which it is created,and this Key cannot be used in any other plain *** boundaries of each Hill text work up to that *** techniques used in this paper are compared with those used in various papers and discussed that how far the current algorithm is better than all other algorit

关键词： Cryptography symmetric cipher text encryption matrix cipher encoding decoding hill matrix 64-bit encryption

来源：评论

学校读者我要写书评

暂无评论

Multi-Class Threat Detection Using Neural Network and Machine Learning Approaches in Kubernetes Environments 6

Multi-Class Threat Detection Using Neural Network and Machin...

引用

6th International Conference on Computing and Informatics, ICCI 2024

作者： Aly, Abdelrahman Fayez, Mahmoud Al-Qutt, Mirvat Hamad, Ahmed M. Dept. Computer Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9798350373875

Kubernetes, an open-source platform for automating deployment, scaling, and management of containerized applications, has become a cornerstone in modern IT infrastructure. Alongside its widespread adoption, Kubernetes faces a series of sophisticated security challenges, especially in managing numerous containers. This research uniquely focuses on multi-class threat detection, classifying various types of security threats within Kubernetes environments. Machine learning is emerging as a powerful tool in cybersecurity, offering new ways to detect and mitigate threats. However, there is a shortage of comprehensive research on its application within Kubernetes, especially for detecting multiple types of security threats. This research aims to bridge this gap by introducing a machine learning-based technique for improved threat detection in Kubernetes environments. We propose an advanced detection method using the Naive Bayes algorithm, complemented by comprehensive feature engineering and dimensionality reduction using neural networks. The most effective model, which combines Principal Component Analysis (PCA) and Autoencoder with the Naive Bayes classifier, achieved an Fl Score of 0.95 and an accuracy of 91%. © 2024 IEEE.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

A Survey on Symbolic Knowledge Distillation of Large Language Models

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第12期5卷 5928-5948页

作者： Acharya, Kamal Velasquez, Alvaro Song, Houbing Herbert Department of Information Systems BaltimoreMD21250 United States University of Colorado Department of Computer Science BoulderCO80309 United States

This survey article delves into the emerging and critical area of symbolic knowledge distillation in large language models (LLMs). As LLMs such as generative pretrained transformer-3 (GPT-3) and bidirectional encoder representations from transformers (BERT) continue to expand in scale and complexity, the challenge of effectively harnessing their extensive knowledge becomes paramount. This survey concentrates on the process of distilling the intricate, often implicit knowledge contained within these models into a more symbolic, explicit form. This transformation is crucial for enhancing the interpretability, efficiency, and applicability of LLMs. We categorize the existing research based on methodologies and applications, focusing on how symbolic knowledge distillation can be used to improve the transparency and functionality of smaller, more efficient artificial intelligence (AI) models. The survey discusses the core challenges, including maintaining the depth of knowledge in a comprehensible format, and explores the various approaches and techniques that have been developed in this field. We identify gaps in current research and potential opportunities for future advancements. This survey aims to provide a comprehensive overview of symbolic knowledge distillation in LLMs, spotlighting its significance in the progression toward more accessible and efficient AI systems. © 2024 IEEE.

关键词： Distillation

来源：评论

学校读者我要写书评

暂无评论

Exploring the Effectiveness of Graph-based Computational Models in COVID-19 Research

引用

Operations Research Forum 2024年第3期5卷 80页

作者： Boadu, Dennis Opoku Appati, Justice Kwame Mensah, Joseph Agyapong Department of Computer Science University of Ghana Accra Ghana Department of Computer Science and Information Systems Ashesi University Berekuso Ghana

The world has witnessed various scientific disciplines’ rapid growth and advancement, leading to groundbreaking discoveries and advances in multiple fields in recent years. One such field that has gained significant attention, particularly during the COVID-19 pandemic, is the application of graph theory techniques in studying the spread and mitigation of the virus. In this paper, we delve into the intricacies of graph theory and its utilization in analyzing COVID-19, shedding light on the innovative approaches researchers worldwide employ. Also, the study evaluates the various implementation of graph theories in spreading and controlling the virus using diverse datasets. The researchers retrieved several works in the COVID-19 and graph theory field from digital databases. However, studies deducted that GT approaches, algorithms and techniques offer insights into transmission hotspots, spread dynamics in social, control and mobility networking, vaccination optimization, evaluation of interventions and epidemic prediction, among other valuable findings. Limitations and future directions were also directed in the study. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Control and mobility networks COVID-19 Edges Graph Theory (GT) Nodes Shortest path (Djikstra) Social

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：