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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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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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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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A Plastic Waste Hotspot Detection System and Its Application to Measuring Environmental Improvement 1

A Plastic Waste Hotspot Detection System and Its Application...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Sasaki, Shiori Nakamura, Ryota Wibowo, Prasetyo Musashino University Department of Data Science Tokyo Japan Musashino University Faculty of Data Science Tokyo Japan Politeknik Elektronika Negeri Department of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350365191

This paper describes a Plastic Waste Hotspot Detection System which has been developed in an international collaborative research project to realize an 'Environmental AI-Human Actions integration' with marine plastic waste reduction actions. In this project, various local conditions are obtained to the system to realize an environmentally resilient society. The nature conservation activities such as marine plastic waste collection and reduction actions are implemented based on these conditions. In the process of integration of Environmental AI and Human Actions, the effects of these activities and activists' contribution to environmental improvement and global-scale knowledge are accumulated, shared, analyzed, and visualized through global-scale analysis. This paper describes the Plastic Waste Hotspot Detection system as one of the systems that integrate AI systems and human actions. This paper examines the feasibility and effectiveness of the system through the fieldwork tests in Phuket, Thailand and Surabaya, Indonesia. © 2024 IEEE.

关键词： Plastic recycling

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data Analysis with Model Explainability for Air Quality and Pollution Prediction of Urban Areas in Edge Networks 12th

Data Analysis with Model Explainability for Air Quality an...

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12th International Conference on Big data and Artificial Intelligence, BDA 2024

作者： Singh, Akansha Chakraborty, Soumendu Adhikari, Mainak Department of Computer Science Indian Institute of Information Technology Lucknow Lucknow India School of Data Science Indian Institute of Science Education and Research Thiruvananthapuram Thiruvananthapuram India

ISBN: (纸本)9783031818202

Air quality significantly impacts human health and economic conditions, making precise and timely assessment crucial in urban areas. Existing studies often fail to predict pollution accurately in smaller areas due to varying meteorological conditions. Most existing prototypes rely on standard ML models, trained with generic, publicly available datasets, which often lack local dynamics, adaptability, and synchronization. To address these challenges, in this paper, we propose a new Hybrid Edge-Cloud Air Quality Monitoring System. For initial model training a robust testbed is created by data Fusion of air quality data from diverse sources like Application Programming Interfaces and public datasets. The system trains and refines ML models in the cloud, identifies key features to reduce feature dimensions, and leverages local decision-making using site-specific data collected by sensors on edge devices. Sensors monitor local environmental parameters for real-time analysis and decision-making, ensuring accurate predictions for specific urban locations while optimizing performance and minimizing resource usage on edge devices. Explainable AI techniques improve transparency and confidence in decisions. The model is validated by simulating various ML models on the testbed, with and without feature extraction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific Computing

Octopus: Experiences with a Hybrid Event-Driven Architecture...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Pan, Haochen Chard, Ryan Zhou, Sicheng Kamatar, Alok Vescovi, Rafael Hayot-Sasson, Valerie Bauer, Andre Gonthier, Maxime Chard, Kyle Foster, Ian Southern University of Science and Technology Department of Computer Science China Illinois Institute of Technology Department of Computer Science ChicagoIL United States University of Chicago Department of Computer Science ChicagoIL United States Argonne National Laboratory Data Science and Learning Division LemontIL United States

ISBN: (纸本)9798350355543

Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distributed research infrastructures by enabling the organization, communication, processing, reliability, and security of events generated from many sources. To support the development of scientific EDA, we introduce Octopus, a hybrid, cloud-to-edge event fabric designed to link many local event producers and consumers with cloud-hosted brokers, and to provide a fabric for developing resilient applications. Octopus can be scaled to meet demand, permits the deployment of highly available Triggers for automatic event processing, and enforces fine-grained access control. We identify requirements in self-driving laboratories, scientific data automation, online task scheduling, epidemic modeling, and dynamic workflow management use cases, and present results demonstrating Octopus' ability to meet those requirements. Octopus supports producing and consuming events at a rate of over 4.2 M and 9.6 M events per second, respectively, from distributed clients. © 2024 IEEE.

关键词： Event-driven architecture research automation scientific computing

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A Novel Pipeline for Virus Integration Sites Detection in Tumor Genomes Using Deep Learning

A Novel Pipeline for Virus Integration Sites Detection in Tu...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Williams, Lorrayya Gupta, Vibhuti Meharry Medical College School of Applied Computational Sciences Department of Computer Science & Data Science NashvilleTN United States

ISBN: (纸本)9798350362480

Cancer is one of the leading causes of death worldwide. Pathogenic viruses are estimated to be responsible for 15% of all human cancers globally and pose significant threats to public health. Viruses integrate their genetic material into the host genome, increasing the risk of cancer promoting changes in it. To understand the molecular mechanisms of virus-mediated cancers, it is crucial to identify viral insertion sites in cancer genomes. However, this effort is hindered by the rapidly increasing volume of tumor sequencing data, along with the challenges of accurate data analysis caused by high viral mutation rates and the difficulty of aligning short reads to the reference genome. Thus it is crucial to develop an efficient method for virus integration site detection in tumor genomes. This paper proposes a novel pipeline to identify viral integration sites leveraging deep Convolutional Neural Networks (CNN). Our contributions are twofold: (i) We propose and integrate three novel matrix generation methods into the pipeline, developed after aligning the host and viral genomes with their respective reference genomes.;(ii) We employ one-hot encoded images with reduced computational complexity to represent viral integration sites and harness the capabilities of Deep CNN networks for detection. The paper illustrates our proposed approach and presents experiments conducted using both synthetic and real sequencing data. Our preliminary experimental results are promising, showcasing the effectiveness of the proposed methods in detecting viral integration sites. © 2024 IEEE.

关键词： Convolutional neural networks

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