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Group Recommendation Method Based on Graph Neural Network

Group Recommendation Method Based on Graph Neural Network

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2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Lu, Mengye Zhu, Peng Miao, Chun Wang, Youjian Wang, Jiacun Nanjing University of Science and Technology School of Economics and Management Nanjing China Monmouth University Department of Computer Science and Software Engineering NJ United States

ISBN: (纸本)9798350376739

Group activities are becoming more and more common on the Internet in the big data environment. Which makes many scholars focus on how to recommend items or activities to a group. However, conventional recommendation systems grapple with issues like data sparsity and the cold start problem, which limits the relevance and accuracy of recommendation. Therefore, this paper conducts an in-depth exploration of group recommendation methods utilizing graph neural networks and introduces a novel recommendation framework aimed at enhancing the efficacy of group recommendations. Firstly, this paper discusses how to construct an effective graph structure to encode the social interaction and individual characteristics of users. Then, an improved graph neural network architecture is proposed. Finally, extensive experimental evaluation is carried out on actual datasets, and the findings demonstrate that our framework markedly enhances the accuracy of recommendations. © 2024 IEEE.

关键词： Graph neural network Group recommendation Recommendation system

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Decision support system of staff member promotion based on cloud computing (Case Study University Of Benghazi) 3

Decision support system of staff member promotion based on c...

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3rd IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Alfsai, Entesar H. Bozed, Kenz A. Hagal, Mohamed A. University of Benghazi Department of Software Engineering Benghazi Libya University of Benghazi Department of Computer Science Benghazi Libya

ISBN: (纸本)9798350319897

The scientific council at Benghazi University suffered from issues related to the scientific promotion process for faculty members. Promotion is still done manually, without a computerized system. Therefore, it faces obstacles in obtaining basic information, issuing promotion decisions, making managerial procedures, lack of flexibility, and the accumulation of papers. There are also unclear biases in decision-making and mechanisms of evaluating promotion criteria. Thus, they need to keep up with the international universities in the promotion process to be successful in administrative business today. Benghazi University needs an information system that can support decision-making, which is a major problem often occurs in the process of promotion of faculty *** use of decision support systems is a solution to reduce subjectivity in decision-making. This work presents an E-Gate that can assist in the promotion of educational staff. It is a system able to provide the best alternative in promotion educational staff. With the decision support system, the promotion process is carried out via the E-Gate using the AHP method according to several criteria. It is stored in the cloud computing because many enterprises are adopting the cloud-computing platform to examine the benefits of self-service, user guide, and high flexibility. The time is right for the educational universities to derive benefits of the cloud, as they are obliged to deliver better ubiquitous services. The study also developed Search Engine to verify the submitted research. © 2023 IEEE.

关键词： Decision support systems

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Forecasting the Stability of COVID-19 Vaccine Companies Stock Market using LSTM and Time-series Models 3

Forecasting the Stability of COVID-19 Vaccine Companies Stoc...

引用

3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Khan, Rahemeen Baloch, Safia Bahria University Department of Software Engineering Karachi Pakistan Fast University Department of Computer Science Karachi Pakistan

ISBN: (纸本)9798350332391

Stock price forecasting is challenging because stock markets are volatile and nonlinear in nature. With the impact of the Covid-19 outbreak in 2019, stock market forecasting has become more complicated. The goal of this study is to identify the top brands in the global covid-19 vaccination market and their impact on the stock market. By forecasting the next months stock exchange, traders will be able to select the most suitable company to invest in. In this paper, MSE, RMSE, and MAPE are used as evaluation matrices to compare timeseries and neural network models such as Arima, Prophet, and LSTM. According to the results of a comparative analysis, the LSTM model produced the most accurate predictions on majority stock prices dataset for the selected vaccination brands. Brand stability is achieved by predicting future stock exchange prices using an outperformed LSTM model. As a result, BioNTech has been identified as a high potential stable brand that the buyer can invest to gain enormous returns among the massive number of probabilistic events. © 2023 IEEE.

关键词： Time series

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A computer Vision-Based Vehicle Detection System Leveraging Deep Learning 1

A Computer Vision-Based Vehicle Detection System Leveraging ...

引用

1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Khan, Saadullah Vohra, Sanayna Siddique, Sultan Abdullah Abro, Abdul Ahad Ebrahim, Mansoor Iqra University Main Campus Department of Software Engineering Karachi Pakistan Iqra University Main Campus Department of Computer Science Karachi Pakistan

ISBN: (纸本)9798350373042

This study uses advanced computer vision (CV) and deep learning (DL) techniques to present a combined approach for extensive vehicle detection and number plate recognition. Improving the effectiveness and precision of traffic surveillance and administration systems is the main goal. The two primary parts of the suggested system are identification of number plates recognition and vehicle detection. The vehicle detection module uses rigid images or video streams to recognize along with monitor vehicles in real-time using innovative DL algorithms. Such limitations of system reliability are overcome through the application of CNN and object detection models in an integrated computer vision system that allows for reliable performance under a variety of environmental conditions including unfavorable lighting and weather situations. The goal of an identification plate recognition feature is to read and extract alphanumeric characters from license plates that have been detected. To precisely identify and interpret license plate data, this stage combines DL models with the use of optical character recognition (OCR) techniques. The system can adapt to different geographical locations by supporting changes in plate layouts, fonts, and positions. By combining these two modules, a complete intelligent transportation system solution is produced, offering useful information for applications such as congestion examination, safety, and assurance. Because of its modular architecture, the system is easily scalable and customizable, enabling it to be adjusted to various hardware configurations and surveillance scenarios. Using real-world scenarios and benchmark datasets, extensive experiments are conducted to assess the effectiveness of the proposed system. The outcomes show excellent efficiency and accuracy in tasks involving the detection of vehicles and number plate recognition. By promoting enhanced security, integrity, and general traffic management, the suggested integrated syste

关键词： Object detection

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System for Table Run Strategy in Peruvian Billiards with Real-Time Oject Detection using YOLOv8n 10

System for Table Run Strategy in Peruvian Billiards with Rea...

引用

10th International Conference on Optimization and Applications, ICOA 2024

作者： Silva, Italo Chavez, Raquel Castillo-Sequera, José Luis Wong, Lenis Peruvian University of Applied Sciences Department of Software Engineering Lima Peru University of Alcalá Department of Computer Science Alcalá de Henares Spain

ISBN: (纸本)9798350387353

The main challenge for billiard players, regardless of their skill level, is to develop a strategy that allows them to consecutively pocket balls in a single turn, commonly known as 'closing the table.' This strategy requires high precision and consistency, especially for less experienced players. This study integrates a YOLOv8n model, a shot suggestion algorithm, and a shot prediction algorithm to build a system that aids players in developing this 'table closing' strategy through visual guides indicating the shots to take and where to position the cue ball for the next shot. The proposed system follows these phases: (1) Selection of the video camera, (2) Selection of the object detection technique, (3) Development of the shot suggestion algorithm, (4) Development of the shot prediction algorithm, and (5) System construction. Phases (1) and (2) address the implementation of the YOLOv8n model, while phases (3) and (4) involve the development of algorithms for the 'table closing' strategy. To validate the system's effectiveness, an experiment with 24 players of different skill levels was conducted, showing that the system reduced the average number of shots needed to close a table to 22, compared to the 47-shot average of professional players. Beginners accepted 100% of the system's recommendations, while intermediate and professional players showed acceptance rates of 95.1% and 95.6%, respectively. A survey indicated high overall satisfaction, with a median response of 4.5 on a 5-point scale. The system also demonstrates significant competitive advantages over existing solutions by maintaining high detection and prediction accuracy while effectively functioning in real-world scenarios, leveraging the latest advances in computer vision to surpass older techniques in both effectiveness and efficiency. © 2024 IEEE.

关键词： computer vision

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Variational Auto-Encoder Model and Federated Approach for Non-Intrusive Load Monitoring in Smart Homes 28

Variational Auto-Encoder Model and Federated Approach for No...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Kaspour, Shamisa Yassine, Abdulsalam Department of Computer Science Thunder Bay Canada Lakehead University Department of Software Engineering Thunder Bay Canada

ISBN: (纸本)9798350300482

Non-Intrusive Load Monitoring (NILM) is a technique used for identifying individual appliances' energy consumption from a household's total power usage. This study examines a novel energy disaggregation model called Variational Auto-Encoder (VAE) with Federated Learning (FL). Specifically, VAE has a complex structure that resolves the issues in Short Sequence-to-Point (Short S2P) with fewer samples as input windows for each appliance. Short S2P cannot be generalized and might confront some challenges while disaggregating multi-state appliances. To this end, we examine a series of experiments using a real-life dataset of appliance-level power from the UK: UK-DALE. We also investigate additional protection of model parameters using Differential Privacy (DP). The findings show that FL with the VAE model achieves comparable performance to its centralized counterpart and improves all the metrics significantly compared to the Short S2P model. © 2023 IEEE.

关键词： Energy utilization

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Predictive Modeling of Students' Stress Levels Using Machine Learning Algorithm 4

Predictive Modeling of Students' Stress Levels Using Machine...

引用

4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Ali, Hassan Amin, M. Hamza Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan GULF University for Sciences and Technology Department of Computer Science Mubarak Al-Abdullah Kuwait

ISBN: (纸本)9798350353266

The increasing prevalence of stress among university students has raised concern about its impact on academic performance and overall well-being. This conference paper explores the applications of machine learning algorithms to predict and analyze the student's stress level. A comprehensive dataset encompassing various stress-related factors, including academic workload, social interactions, and lifestyle, was collected from Kaggle which consists of 1100 instances and 19 attributes. Our methodology places a premium on human connection, integration of qualitative data to refine the accuracy of stress predictions. By seamlessly merging the capability of machine learning with an empathetic understanding of the human experience, our approach strives to pave the way for a more holistic and personalized educational ecosystem. The insights derived from this study aspire to guide educators, administrators, and policy makers in crafting nurturing environments that empower students to excel both academically and emotionally. Embarking on a journey to unravel the myriad factors influencing students' stress, we consider academic pressures, social dynamics, and personal experiences. Harnessing machine learning algorithms such as decision trees, neural networks, we navigate expansive student populations. Acknowledging the nuanced nature of stress, our study integrates a compassionate approach with cutting-edge technology. The study employs state-of-the-art machine learning techniques using various classification algorithms, to build predictive models for assessing stress levels out of which random forest gives 97.45% accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Decentralized Face Identification with Hierarchical Navigable Small World on Blockchain

Decentralized Face Identification with Hierarchical Navigabl...

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2023 International Conference on Machine Learning and Automation, CONF-MLA 2023

作者： Lee, Hsiang-Hung Chen, Yiting Department of Computer Science & Information Engineering National Taiwan Normal University Taipei City106 Taiwan Department of Computer Software and Theory Donghua University Shanghai201620 China

This paper presents a novel method for decentralized storage in deep-learning-based face recognition systems using the Hierarchical Navigable Small World (HNSW) algorithm. The proposed solution utilizes Ethereum smart contracts, which acts as highly available data storage systems for storing identifiable data for authorized personnel. In addition, the solution is integrated with a centralized vector database that is in charge of vector indexing, searching and associating face embeddings to an identity on the Ethereum blockchain with anonymous hashes. Vector indexing and search processes involve different machine learning algorithms that enable computations to be carried out in a reasonable time with good matching accuracy. Specifically, we compared different approaches and selected the HNSW algorithm. Accordingly, we successfully implemented a prototype of a reliable and privacy-focused decentralized face identification system for areas under government surveillance, such as customs inspection sites. In our measurements, the system could handle 20,000 face vectors easily with high matching accuracy, and the performance could be further improved using more powerful hardware. Finally, we also propose additional methods to further scale up the system to handle millions of face vectors. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Blockchain

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Voice Presentation Attacks Detection using Acoustic MLTP Features and BiLSTM 3

Voice Presentation Attacks Detection using Acoustic MLTP Fea...

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3rd International Conference on Communication, Computing and Digital Systems, C-CODE 2023

作者： Ibrar, Sundas Javed, Ali Ilyas, Hafsa Uet Taxila Department of Computer Science Punjab Pakistan Uet Taxila Department of Software Engineering Punjab Pakistan

ISBN: (纸本)9798350332391

Automatic speaker verification (ASV) systems have been used in numerous voice-controlled technologies including voice banking, automobiles, smartphones, smart homes, etc. The growth of voice-operated devices is increasing exponentially, and it is mandatory to secure the ASV systems against voice spoofing attacks such as cloned and synthetic voices. The research community has worked to develop spoofing countermeasures to improve the security of ASV systems. However, existing spoofing countermeasures have limitations to detect voice conversion, synthetic speech, and voice replay attacks. There still exists a need for the development of voice spoofing detectors to further strengthen the security of voice-controlled systems. Therefore, in this study, we designed a novel spoofing countermeasure for identifying physical access (PA) and logical access (LA) attacks. More precisely, we proposed a novel acoustic feature descriptor, namely, modified local ternary patterns (MLTP), and used them to train a BiLSTM classifier for voice spoofing detection. We assessed the performance of our approach using the standard ASVspoof-2019 dataset and attained an equal error rate (EER) of 1.87% and 2.66% for the PA and LA attacks, respectively. Experimental results obtained from a detailed assessment signify that our approach has the capability to accurately classify the bonafide and spoofed audio samples over contemporary methods. © 2023 IEEE.

关键词： Automation

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TezNet: An Efficient Convolutional Neural Network for Optical Coherence Tomography Classification 6

TezNet: An Efficient Convolutional Neural Network for Optica...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Qaiser, Muhid Abbas, Muhammad Sohail Muhammad, Asif Haider, Khadija Khan, Haroon National University of Computer and Emerging Sciences Department of Artificial Intelligence and Data Science Islamabad Pakistan National University of Computer and Emerging Sciences Department of Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Retinal diseases such as Choroidal Neovascularization (CNV), Drusen, and Diabetic Macular Edema (DME) can severely impair vision and are challenging to reverse if detected late. Optical Coherence Tomography (OCT) serves as a non-invasive imaging modality to diagnose these conditions through cross-sectional retinal images. Current advancements in retinal disease detection predominantly focus on fine-tuning larger models to achieve high accuracy, often at the expense of substantial training time and data requirements. We introduce an innovative Convolutional Neural Network (CNN) named TezNet that is deeper and wider than existing custom CNNs for the dataset used in this research yet maintains a lightweight architecture with only 3.6 million parameters. This high efficiency is accomplished by leveraging depthwise and spatial separable convolutional techniques without employing preprocessing techniques or noise reduction filters. The data augmentation techniques generate diverse image qualities, ensuring robust predictions across various scenarios. By training exclusively on a single dataset, our model achieves rapid training times and optimal resource utilization, minimizing the risk of overfitting. This blend of advanced parameter optimization, minimal preprocessing, and strategic data augmentation underscores the potential of our CNN model for swift, precise, and resource-efficient retinal disease detection. TezNet model achieves a remarkable 96% accuracy, competing closely with state-of-the-art networks like ResNet50 (over 23M parameters), InceptionV3 (over 25M parameters), and ResNeXt (89M parameters). © 2024 IEEE.

关键词： Optical coherence tomography

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