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2024 IEEE International Conference on Computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (AI) advances, it is essential to continuously comprehend its limitations to optimise the integration of AI into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and AI, focusing on the differences and identifying limitations of AI from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of AI into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each AI limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness AI’s computational power and algorithmic innovations to enhance AI’s overall performance and applicability. ©2024 IEEE.

关键词： Emotion Recognition

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Multi-modality Meets Re-learning: Mitigating Negative Transfer in Sequential Recommendation

arXiv

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arXiv 2023年

作者： Peng, Bo Parthasarathy, Srinivasan Ning, Xia The Department of Computer Science and Engineering The Ohio State University ColumbusOH43210 United States The Department of Computer Science and Engineering The Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States The Department of Biomedical Informatics The Department of Computer Science and Engineering The Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

Learning effective recommendation models from sparse user interactions represents a fundamental challenge in developing sequential recommendation methods. Recently, pre-training-based methods have been developed to tackle this challenge. Though promising, in this paper, we show that existing methods suffer from the notorious negative transfer issue, where the model adapted from the pre-trained model results in worse performance compared to the model learned from scratch in the task of interest (i.e., target task). To address this issue, we develop a method, denoted as ANT, for transferable sequential recommendation. ANT mitigates negative transfer by 1) incorporating multi-modality item information, including item texts, images and prices, to effectively learn more transferable knowledge from related tasks (i.e., auxiliary tasks);and 2) better capturing task-specific knowledge in the target task using a re-learning-based adaptation strategy. We evaluate ANT against eight state-of-the-art baseline methods on five target tasks. Our experimental results demonstrate that ANT does not suffer from the negative transfer issue on any of the target tasks. The results also demonstrate that ANT substantially outperforms baseline methods in the target tasks with an improvement of as much as 15.2%. Our analysis highlights the superior effectiveness of our re-learning-based strategy compared to fine-tuning on the target tasks. © 2023, CC BY.

关键词： Recommender systems

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MVLA-Net:A Multi-View Lesion Attention Network for Advanced Diagnosis and Grading of Diabetic Retinopathy

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computers, Materials & Continua 2025年第4期83卷 1173-1193页

作者： Tariq Mahmood Tanzila Saba Faten S.Alamri Alishba Tahir Noor Ayesha Artificial Intelligence and Data Analytics(AIDA)Lab College of Computer&Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Sciences University of EducationVehari CampusVehari61100Pakistan Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia Shifa College of Medicine Shifa Tameer-E-Millat UniversityIslamabad44000Pakistan Center of Excellence in Cyber Security(CYBEX) Prince Sultan UniversityRiyadh11586Saudi Arabia

Innovation in learning algorithms has made retinal vessel segmentation and automatic grading tech-niques crucial for clinical diagnosis and prevention of diabetic *** traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic *** the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data,dual-modal diabetic retinopathy grading methods offer superior ***,the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to multi-scale *** paper addresses these issues by focusing on multi-scale retinal vessel segmentation,dual feature fusion,data augmentation,and attention-based *** proposed model aims to improve comprehensive segmentation for retinal images with varying vessel *** employs a dual-branch parallel architecture that integrates a transformer encoder with a convolutional neural network encoder to extract local and global information for synergistic saliency *** that,the model uses residual structures and attention modules to extract critical lesions,enhancing the accuracy and reliability of diabetic retinopathy *** evaluate the efficacy of the proposed approach,this study compared it with other pre-trained publicly open models,ResNet152V2,ConvNext,Efficient Net,DenseNet,and Swin Transform,with the same developmental *** models achieved approximately 85%accuracy with the same image preparation ***,the proposed approach outperforms and optimizes existing models by achieving an accuracy of 99.17%,99.04%,and 99.24%,on Kaggle APTOS19,IDRiD,and EyePACS datasets,*** results support the model’s utility in helping ophthalmologists diagnose diabetic retinopathy more rapidly and accurat

关键词： Diabetic retinopathy grading retinal vessel segmentation dual-modal deep learning attention mecha-nism health risks

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Detection of Plant Disease Using Machine Learning and Deep Learning Algorithms

Detection of Plant Disease Using Machine Learning and Deep L...

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International Conference on science Technology Engineering and Management (ICONSTEM)

作者： Kalyani Gollapudi Preethi Varigonda S. Jancy S. PushpaLatha Viji Amutha Mary.A Suji Helen Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Data Analytics Saveetha School of Engineering SIMATS Chennai

Agriculture performs an critical position in India's economic system. Early detection of plant illnesses is critical to save you crop damage and similarly spread of diseases. Most plants, along with apple, tomato, cherry, grape, show symptoms of leaf ailment. These visible patterns may be found to correctly predict the disorder and take early movement to save you it. This can be triumph over with system getting to know and deep getting to know algorithms. We therefore recommend a method that determines tomato plant disease from pix of leaves. This method is performed with aid vector device (SVM), random woodland gadget studying algorithm, and look at algorithms Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and ResNet that is one of the switch learning techniques. Snoring. After the facts set is processed by way of the algorithms, the accuracy of the algorithms is compared and the snapshots are categorized.

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Almost Minimax Optimal Best Arm Identification in Piecewise Stationary Linear Bandits

arXiv

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arXiv 2024年

作者： Hou, Yunlong Tan, Vincent Y.F. Zhong, Zixin Department of Mathematics National University of Singapore Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore Singapore Data Science and Analytics Thrust Hong Kong University of Science and Technology Guangzhou China

We propose a novel piecewise stationary linear bandit (PSLB) model, where the environment randomly samples a context from an unknown probability distribution at each changepoint, and the quality of an arm is measured by its return averaged over all contexts. The contexts and their distribution, as well as the changepoints are unknown to the agent. We design Piecewise-Stationary Ε-Best Arm Identification+ (PSΕBAI+), an algorithm that is guaranteed to identify an Ε-optimal arm with probability ≥ 1 - δ and with a minimal number of samples. PSΕBAI+ consists of two subroutines, PSΕBAI and NAÏVE Ε-BAI (NΕBAI), which are executed in parallel. PSΕBAI actively detects changepoints and aligns contexts to facilitate the arm identification process. When PSΕBAI and NΕBAI are utilized judiciously in parallel, PSΕBAI+ is shown to have a finite expected sample complexity. By proving a lower bound, we show the expected sample complexity of PSΕBAI+ is optimal up to a logarithmic factor. We compare PSΕBAI+ to baseline algorithms using numerical experiments which demonstrate its efficiency. Both our analytical and numerical results corroborate that the efficacy of PSΕBAI+ is due to the delicate change detection and context alignment procedures embedded in PSΕBAI. © 2024, CC BY.

关键词： Probability distributions

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Harnessing data analytics for Enhanced Decision-Making in Business Operations

Harnessing Data Analytics for Enhanced Decision-Making in Bu...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Ajatray Swagat Bhuyan Savita Gehlawat Aarti Pardeshi Gauri Chetan Deshpande Ritu Department of Computer Science & Engineering Chandigarh University Mohali Punjab India Department of Business Analytics ITM Skills University Maharashtra India Computer Science Engineering (Data Science) Saraswati Engineering College Maharashtra India Department of Computer Science & Engineering Chandigarh College of Engineering Chandigarh Group of Colleges Jhanjeri India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Businesses are using data analytics more and more in the age of digital transformation to inform strategic choices and maximize operational effectiveness. The goal of this research study is to improve decision-making processes in corporate operations via the integration of modern data analytics approaches. Through the analysis of case studies from a range of sectors, we demonstrate how essential data analytics is for seeing patterns, projecting results, and gaining useful knowledge. As we explore data collecting, processing, and analysis techniques, highlighting the significance of data integrity and quality. The use of artificial intelligence (AI) technologies and machine learning algorithms to automate decision-making, lower human error, and speed up company procedures is also covered in the article. The essential elements-organizational culture, technology infrastructure, and workforce competencies, among others-that make up effective data analytics efforts via a thorough literature research and practical analysis. Based on the results, companies who use data analytics well may enhance their agility in adapting to market changes and get a competitive advantage. data privacy, security, and prejudice are among the ethical issues and concerns related to data analytics that are finally covered in this study. Businesses may handle these obstacles and optimize the advantages of data- driven decision-making by following the recommendations given.

关键词： Ethics Technological innovation data analysis Decision making Companies Stakeholders Security Artificial intelligence Protection Business

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Parm: Efficient Training of Large Sparsely-Activated Models with Dedicated Schedules

arXiv

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arXiv 2024年

作者： Pan, Xinglin Lin, Wenxiang Shi, Shaohuai Chu, Xiaowen Sun, Weinong Li, Bo Data Science and Analytics Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science Hong Kong Baptist University Hong Kong School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Sparsely-activated Mixture-of-Expert (MoE) layers have found practical applications in enlarging the model size of large-scale foundation models, with only a sub-linear increase in computation demands. Despite the wide adoption of hybrid parallel paradigms like model parallelism, expert parallelism, and expert-sharding parallelism (i.e., MP+EP+ESP) to support MoE model training on GPU clusters, the training efficiency is hindered by communication costs introduced by these parallel paradigms. To address this limitation, we propose Parm, a system that accelerates MP+EP+ESP training by designing two dedicated schedules for placing communication tasks. The proposed schedules eliminate redundant computations and communications and enable overlaps between intra-node and internode communications, ultimately reducing the overall training time. As the two schedules are not mutually exclusive, we provide comprehensive theoretical analyses and derive an automatic and accurate solution to determine which schedule should be applied in different scenarios. Experimental results on an 8-GPU server and a 32-GPU cluster demonstrate that Parm outperforms the state-of-the-art MoE training system, DeepSpeed-MoE, achieving 1.13× to 5.77× speedup on 1296 manually configured MoE layers and approximately 3× improvement on two real-world MoE models based on BERT and GPT-2. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Lactose prediction in dry milk with hyperspectral imaging: A data analysis competition at the "International Workshop on Spectroscopy and Chemometrics 2024"

Lactose prediction in dry milk with hyperspectral imaging: A...

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作者： Frizzarin, Maria Caponigro, Vicky Domijan, Katarina Molle, Arnaud Aderinola, Timilehin Nguyen, Thach Le Serramazza, Davide Ifrim, Georgiana Konkolewska, Agnieszka Irish Cattle Breeding Federation Highfield House Bandon Ireland School of Biosystems and Food Engineering Belfield Dublin Ireland Department of Pharmacy University of Salerno Fisciano Italy Department of Mathematics and Statistics National University of Ireland Maynooth Ireland Department of Veterinary Science University of Parma Parma Italy School of Computer Science University College Dublin Belfield Dublin Ireland Insight SFI Research Centre for Data Analytics University College Dublin Belfield Dublin Ireland

In April 2024, the Vistamilk SFI Research Centre organized the fourth edition of the "International Workshop on Spectroscopy and Chemometrics — Spectroscopy meets modern Statistics". Within this event, a data challenge was organized among workshop participants, focusing on hyperspectral imaging (HSI) of milk samples. Milk is a complex emulsion comprising of fats, water, proteins, and carbohydrates. Due to the widespread prevalence of lactose intolerance, precise lactose quantification in milk samples became necessary for the dairy industry. The dataset provided to the participants contained spectral data extracted from HSI, without the spatial information, obtained from 72 samples with reference laboratory values for lactose concentration [mg/mL]. The winning strategy was built using ROCKET, a convolutional-based method that was originally designed for time series classification, which achieved a Pearson correlation of 0.86 and RMSE of 9.8 on the test set. The present paper describes the approaches and statistical methods adopted by all the participants to analyse the data and develop the lactose prediction models. © 2024

关键词： Hyperspectral imaging

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Phishing Web Page Detection using Web Scraping

Phishing Web Page Detection using Web Scraping

引用

IEEE Southeastcon

作者： Mallika Boyapati Ramazan Aygun School of Data Science and Analytics Kennesaw State University Kennesaw GA USA Department of Computer Science Kennesaw State University Kennesaw GA USA

In today’s Internet era, webpages act as major user interfaces for the applications hosted by the organizations. One of the most common security attacks on the web page applications is phishing attacks. Phishing is a social engineering attack in which an adversary tries to steal user credentials by tricking them to believe that they are on a legitimate web page. Adversaries are using sophisticated and new ways to forge the web page designs craftily to trick the users into visiting the malicious links. The phishing webpages are used as a medium to carry out the art of phishing attacks. Web scraping is a methodology to extract features of each webpage. In this paper, web scraping is employed to extract hybrid feature set to implement Machine Learning (ML) models. The machine learning models like XGBoost, Multilayer Perceptron, Logistic Regression, SVM, Auto Encoder, Random Forest, Decision Trees, K-means, and Naive Bayes are built. All the models are tested with and without applying principal component analysis (PCA), a feature reduction technique. Extracting hybrid features by employing web scraping to train the ML models and finding the key features contributing to phishing detection on Phishpedia, Kaggle, and PhishTank datasets is the key contribution of this paper. Results show that XGBoost algorithm outperformed the all other classifiers with 98% accuracy or higher on the web scraped features on all the datasets. The model achieved higher precision and recall when compared to other approaches like CANTINA+, URL based approaches, SenseInput, Knowing Thy Domain, and Phishpedia on the Phishpedia dataset.

关键词： Uniform resource locators Visualization Machine learning algorithms Web page design Phishing Virtual assistants Web pages

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Default Prediction on Commercial Credit Big data Using Graph-based Variable Clustering

Default Prediction on Commercial Credit Big Data Using Graph...

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International Conference on Semantic Computing

作者： Mallika Boyapati Ramazan Aygun School of Data Science and Analytics Kennesaw State University Kennesaw GA USA Department of Computer Science Kennesaw State University Kennesaw GA USA

Credit-lending organizations have resorted to the use of machine learning (ML) algorithms in the recent past to predict the probability of the default of a business. Explainability of the decisions made by the traditional statistical algorithms like Logit models brings transparency to every stakeholder involved in the process. On the other hand, machine learning models like XGBoost and Neural Nets have achieved better accuracy scores, but their decisions are not easily comprehensible. In this paper, we propose a graph based variable clustering (GVC) method as a filter based approach to select prominent features while retaining as much variance as possible. Our experiments show that our GVC approach is not only almost 40 times faster than the existing variable clustering methods but retains retains 5% more variance than the existing *** feature set from GVC approach has performed better with an increase of 6% accuracy on an average. The predictions on the feature set from GVC were 98% accurate using XGBoost algorithm.

关键词： Machine learning algorithms Runtime Semantics Neural networks Clustering algorithms Machine learning Organizations

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