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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhang, Haoyu Wang, Wenbin Yu, Tianshu School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Computer Science University College London United Kingdom School of Computer Science Wuhan University China

The field of Multimodal Sentiment Analysis (MSA) has recently witnessed an emerging direction seeking to tackle the issue of data incompleteness. Recognizing that the language modality typically contains dense sentiment information, we consider it as the dominant modality and present an innovative Language-dominated Noise-resistant Learning Network (LNLN) to achieve robust MSA. The proposed LNLN features a dominant modality correction (DMC) module and dominant modality based multimodal learning (DMML) module, which enhances the model's robustness across various noise scenarios by ensuring the quality of dominant modality representations. Aside from the methodical design, we perform comprehensive experiments under random data missing scenarios, utilizing diverse and meaningful settings on several popular datasets (e.g., MOSI, MOSEI, and SIMS), providing additional uniformity, transparency, and fairness compared to existing evaluations in the literature. Empirically, LNLN consistently outperforms existing baselines, demonstrating superior performance across these challenging and extensive evaluation metrics. © 2024 Neural information processing systems foundation. All rights reserved.

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Evaluation of REAL-ESRGAN Using Different Types of Image Degradation 11

Evaluation of REAL-ESRGAN Using Different Types of Image Deg...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Andriadi, Kus Zarlis, Muhammad Heryadi, Yaya Chowanda, Andry Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta Indonesia Binus Graduate Program Bina Nusantara University Information System Management Department Jakarta11480 Indonesia School of Computer Science Binus University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350389654

This study evaluates the performance of the REAL-ESRGAN [1] model on images with varying levels of degradation using the DIV2K dataset [2], such as the Wild, the Mild, the Difficult, and the x8 subsets. REAL-ESRGAN was created to solve super-resolution problems and aims to produce high-resolution images from low-resolution images. Experiments were conducted at scales of x2 and x4, and performance was measured using Full-Reference metrics (LPIPS, PSNR, SSIM) and No-Reference metrics (NIQE, MANIQA, CLIPIQA, and PI). The Results were good, especially with the x2 scale;it has higher PSNR and SSIM scores, lower LPIPS and NIQE values, and enhanced visual and perceptual quality. The model faced more significant challenges with the wild and the difficult datasets because they have more complex degradations and compression artifacts;it can be seen with unstable results of Full-Reference and No-Reference metrics. On the contrary, the Mild and x8 datasets yielded better results in both metrics;not only that, even the computational cost for Mild and x8 outperforms the rest of the dataset. This study shows the strengths and limitations of REAL-ESRGAN in handling different levels of image degradation. For future research, the model needs enhancement to tackle the degradation format of the wild and the difficult dataset. It would be good if the REAL-ESRGAN improvement could also maintain the computational cost. © 2024 IEEE.

关键词： Gallium nitride

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Modeling a Novel Hyper-Parameter Tuned Deep Learning Enabled Malaria Parasite Detection and Classification

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computers, Materials & Continua 2023年第12期77卷 3289-3304页

作者： Tamal Kumar Kundu Dinesh Kumar Anguraj S.V.Sudha Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationVaddeswaramAndhra PradeshIndia Department of Artificial Intelligence and Data Science KPR Institute of Engineering and TechnologyCoimbatoreTamil Nadu641407India

A theoretical methodology is suggested for finding the malaria parasites’presence with the help of an intelligent hyper-parameter tuned Deep Learning(DL)based malaria parasite detection and classification(HPTDL-MPDC)in the smear images of human peripheral *** existing approaches fail to predict the malaria parasitic features and reduce the prediction *** trained model initiated in the proposed system for classifying peripheral blood smear images into the non-parasite or parasite classes using the available online *** Adagrad optimizer is stacked with the suggested pre-trained Deep Neural Network(DNN)with the help of the contrastive divergence method to *** features are extracted from the images in the proposed system to train the DNN for initializing the visible *** smear images show the concatenated feature to be utilized as the feature vector in the proposed ***,hyper-parameters are used to fine-tune DNN to calculate the class labels’*** suggested system outperforms more modern methodologies with an accuracy of 91%,precision of 89%,recall of 93%and F1-score of 91%.The HPTDL-MPDC has the primary application in detecting the parasite of malaria in the smear images of human peripheral blood.

关键词： Malaria parasite classification hyper-parameter deep neural network the feature vector

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Prediction of Alzheimer'S Disease from Magnetic Resonance Images (MRI) 5

Prediction of Alzheimer'S Disease from Magnetic Resonance Im...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Manimuthu, A. Peter Jose, P. Gangadevi, E. Kumar, U.Udhaya Anandaraj, M.D. Loyola College Department of Data Science Chennai India Mount Carmel College Department of Computer Science Bangalore India Loyola College Department of Computer Science Chennai India Shanmuga Industries Arts & Science College Department of Mca Tamil Nadu India

ISBN: (纸本)9798350383522

Alzheimer's disease (AD) is an enigmatic, neurodegenerative brain disorder that impairs memory, concentration, and even basic tasks. Every three seconds, a new AD case emerges worldwide, underscoring its widespread impact. Extensive research underscores the pivotal role of early AD detection in curtailing treatment expenses and duration. This study pioneers a dual approach, leveraging both Machine Learning and Deep Learning methodologies to prognosticate Alzheimer's through MRI images. Notably, the VGG16-based model achieves an impressive accuracy of 0.808, surpassing conventional machine learning models (SVM, RF, KNN, DT, NB, LR) and sophisticated deep learning counterparts (CNN, VGG16, ResNet50, MobileNet, DenseNet 121). This outcome presents a pivotal stride toward advancing Alzheimer's diagnosis and optimizing treatment strategies for enhanced patient care. © 2024 IEEE.

关键词： Alzheimer's Disease Deep Learning Machine Learning

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Energy Sector Stock Price Prediction Using the CNN, GRU & LSTM Hybrid Algorithm

Energy Sector Stock Price Prediction Using the CNN, GRU & LS...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Sulistio, Bambang Warnars, Harco Leslie Hendric Spits Gaol, Ford Lumban Soewito, Benfano Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Nowadays, many people are starting to care about early investment. One of the most popular investments lately, especially for millennials, is a stock investment. In investing, there are advantages and risks of loss. One way to reduce the risk of loss is by using price predictions before investing in stocks. This paper proposes the use of deep learning in making stock predictions. We conducted research by calculating the performance of six deep-learning algorithms to predict stock closing prices. The application of the CNN-LSTM-GRU hybrid algorithm combination produces the best performance compared to other methods, based on the value: Root Mean Squared Error (RMSE) decreased by 1.100 by 14%, Mean Absolute Error (MAE) was successfully reduced by 0.798 by 13.4%, and R Square increased by 0.957 by 3.9%. In predicting stock prices on the Indonesian Stock Exchange, especially in the energy sector, CNN-LSTM-GRU is more appropriate for investors than using a single algorithm to make decisions in investing in stocks.. © 2023 IEEE.

关键词： Long short-term memory

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Analyzing Company's Share Purchase Value Based on Financial Reports Using Artificial Neural Network (ANN) 9

Analyzing Company's Share Purchase Value Based on Financial ...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Shawn, Jeremy Gunawan, Jonathan Hao, Chen Zi Anggreainy, Maria Susan Kurniawan, Afdhal Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350370126

Because financial time series forecasting is sensitive to political, economic, and social factors, it is not a simple task. As a result, those who make investments in currency exchange and financial markets typically search for reliable models that can guarantee they will maximize their profile and minimize their losses. Fortunately, many studies have used a method from Artificial Neural Networks (ANNs) called Backpropagation, could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast stock share prediction from closing value of PT. Bank Central Asia Tbk, and PT. Bank Maybank Indonesia Tbk. The results show that the using Backpropagation gives the closest result. And for the rating of judgement for cast accuracy, it exceeded 10% accuracy, which means high accurate from the prediction. For further checking, comparing the results of research from Victor's results, it almost hits the same accuracy percentage. Which means, these prediction are accurate enough to do time series forecasting. © 2023 IEEE.

关键词： Backpropagation

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Seamless High Availability in MySQL database Systems: An Advanced Implementation of ProxySQL, Orchestrator, and Keepalived

Seamless High Availability in MySQL Database Systems: An Adv...

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2025 International Conference on Advancement in data science, E-learning and Information System, ICADEIS 2025

作者： Loa, Jeremy Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331513320

High availability (HA) in MySQL databases remains a critical challenge, particularly due to risks such as single point of failure (SPOF) and limited scalability in existing setups. Previous studies have explored various approaches to address these issues. One study implemented ProxySQL and Orchestrator for query routing and failover but relied on a single server instance, creating a SPOF that could disrupt client access during failures. Another study implemented HAProxy to balance traffic and couple database servers with specific web applications. While effective for certain use cases, this approach reduced resource efficiency and scalability, because it relies on HTTP protocol and database server are coupled to each application, making it less suitable for dynamic, shared environments requiring high flexibility. These limitations highlight the need for a more robust and scalable HA solution. This paper proposes an improved HA architecture that eliminates SPOFs and enhances scalability by combining redundant ProxySQL instances synchronized with Keepalived managed Virtual IP (VIP) and automated failover using Orchestrator. The architecture allows shared database resources across multiple applications while decoupling them from specific application servers. Test results confirm the system's ability to maintain seamless failover, consistent query routing, and uninterrupted client access through dynamic Virtual IP reassignment to backup server. By addressing the issues of previous studies, this solution provides a practical and scalable framework to achieve high availability in the MySQL cluster by combing ProxySQL, Orchestrator, and Keepalived, making it suitable for modern enterprise environments. © 2025 IEEE.

关键词： HTTP

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Content Based Email Spam Filtering and Detection using Hybrid Supervised Learning Approach 7

Content Based Email Spam Filtering and Detection using Hybri...

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7th World Conference on Computing and Communication Technologies, WCCCT 2024

作者： Saquib, Najam U Khalid, Waqar Shaheen, Sahibzadi Annum Asim, Muhammad Ahmad, Naveed Abasyn University Department of Computer Science Islamabad Pakistan Institute of Management Sciences Department of Computer Science Peshawar Pakistan Capital University of Science & Technology Department of Computer Science Islamabad Pakistan Prince Sultan University Eias Data Science and BlockChain Lab College of Computer and Information Science Riyadh11586 Saudi Arabia Prince Sultan University College of Computer and Information Science Riyadh11586 Saudi Arabia

ISBN: (纸本)9798350372328

In today's world, email is used widely for communication purposes globally. Email spam are unwanted emails that are sent to many recipients receivers. It is usually used for commercial purposes More likely spam enail is also used for malicious attempts to gain access to receiver computer/device. This article presents a novel hybrid architecture for spam emails filtering using mails content-based approach. For filtering, Word2Vec (W2V) word embedding procedure is used for feature selection with bio-inspired 'Particle Swarm Optimization (PSO)' and 'Support Vector Machine (SVM)' combined with 'Convolutional Neural Network (CNN)'. Moreover, our proposed hybrid architecture is simulated in open-source tool and the performance is evaluated using standard confusion matrix. Afterw ards, the results are compared with various previous algorithms/models. Experimental results show that our proposed hybrid scheme enhances detection accuracy of spam emails. © 2024 IEEE.

关键词： Support vector machines

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Weight Loss Recommendation System

Weight Loss Recommendation System

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2024 IEEE International Conference on Cybernetics and Innovations, ICCI 2024

作者： Akrungsri, Kitipop Chomphuthan, Thanathat Janthong, Vorakanya Yaembangyang, Wanwera Saiyuang, Worathan Songmuang, Pokpong Thammasat University Data Science And Innovation Pathum Thani Thailand Thammasat University Department Of Computer Science Pathum Thani Thailand

ISBN: (纸本)9798350370096

In the modern days, overweight and obesity are growing fast around the world due to urban growth and shifting lifestyles, eating habits have changed towards unhealthy food, which can lead to overweight and associated health problems. Our food recommendation system is designed to assist users in discovering food that aligns with daily calorie goals. This research study aimed to develop a personalized food recommendation system helping users effectively manage their weight. The study is performed by two different methods. Collaborative filtering finds similar patterns or information of ratings on food items from individual users. Content-based uses similarities in food item that user preferred to make. Both recommendation methods are calculated based on user's calorie needs per day, exercise behavior, and user's most recent meal which users can enter through a user-friendly web-based application. © 2024 IEEE.

关键词： Collaborative filtering

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Product Quality Enhancement in Social Media using Emotion-Enhanced Bert (EE-BERT) Model 2

Product Quality Enhancement in Social Media using Emotion-En...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Raj Kumar, V.S. Kumaresan, T. Mythily, V. Raghavendran, N. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data science Sathyamangalam India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Nandha Engineering College Department of Computer Science and Engineering Erode India Rmk college of Engineering and Technology Department of Artificial Intelligence and Data science Chennai India

ISBN: (纸本)9798350368413

Twitter plays an important role in understanding the consumer sentiment about the products. The advanced analytics and Natural Language Processing (NLP) are used to extract actionable insights from this data and useful for a business to improve features and address issues on time for better customer satisfaction and brand loyalty. Most of the current models suffer from a lack of effective ways to handle subtle sentiment analysis because of a lack of contextual understanding and proper feature extraction. Most traditional models cannot combine and optimize features that lead to obtaining sentiments. This leads to overfitting and problems caused by local minima. The proposed Emotion-Enhanced BERT (EE-BERT) model is an extension of the standard BERT to learn contextual embeddings in user sentiment. In this work, data preprocessing includes text cleaning, tokenization, dealing with emojis and emoticons, and lemmatization with the best quality data. The proposed work is basically a dual-stream architecture extended with butterfly optimizer. First, the advanced tokenization and semantic understanding techniques is used for extracting textual features. Second, the emotion-enhanced features extracted through emotion-hash vectorization to enhance the model's contextual awareness in social media data. This provides a unified framework that combines the feature extraction methods to enhance the accuracy in sentiment analysis. It demonstrated with an impressive accuracy of 98.2% on sentiment in posts and comments with 0.15 losses. The proposed work is free from overfitting and local minima in product quality enhancement. © 2024 IEEE.

关键词： Tweets

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