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IEEE Symposium on computers and Communications (ISCC)

作者： Nasim Mahmud Nayan Ashraful Islam Muhammad Usama Islam Eshtiak Ahmed Mohammad Mobarak Hossain Md Zahangir Alam Department of Computer Science and Engineering University of Information Technology and Sciences Bangladesh Center for Computational and Data Sciences Independent University Bangladesh Department of Computer Science and Engineering Independent University Bangladesh School of Computing and Informatics University of Louisiana at Lafayette USA Department of Computer Science and Engineering Daffodil International University Bangladesh

This study investigated the predictive ability of ten different machine learning (ML) models for diabetes using a dataset that was not evenly distributed. Additionally, the study evaluated the effectiveness of two oversampling and undersampling methods, namely the Synthetic Minority Oversampling Technique (SMOTE) and the Near-Miss algorithm. Explainable Artificial Intelligence (XAI) techniques were employed to enhance the interpretability of the model's predictions. The results indicate that the extreme gradient boosting (XGB) model combined with SMOTE oversampling technique exhibited the highest accuracy and an F1-score of 99% and 1.00 respectively. Furthermore, the utilization of XAI methods increased the dependability of the model's decision-making process, rendering it more appropriate for clinical use. These results imply that integrating XAI with ML and oversampling techniques can enhance the early detection and management of diabetes, leading to better diagnosis and intervention.

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Machine Translation for Morphologically Rich Low-Resourced South African Languages

Machine Translation for Morphologically Rich Low-Resourced S...

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International Multidisciplinary Information Technology and engineering Conference (IMITEC)

作者： Hlaudi Daniel Masethe Lawrence M Mothapo Sunday O. Ojo Pius A. Owolawi Mosima Anna Masethe Fausto Giunchigilia Department of Data Science Tshwane University of Technology South Africa Department of Information Technology Durban University of Technology South Africa Department of Computer Engineering Tshwane University of Technology South Africa Department of Computer Science and Information Technology Sefako Makgatho Health Sciences University South Africa Department of Information Engineering and Computer Science University of Trento Italy

ISBN: (数字)9798350387988

ISBN: (纸本)9798350387995

In the context of machine translation, many of South Africa's languages are categorized as low-resourced, despite the country's vast linguistic diversity. Creating efficient multilingual translation systems that can support languages like Afrikaans, Sesotho sa Leboa (Sepedi), isiNdebele, Setswana, isiXhosa, Xitsonga, siSwati and isiZulu are severely hampered by the lack of high-quality training data. Low translation accuracy and fluency are the result of existing translation models' frequent inability to perform well in these languages. As a result, speakers of these languages encounter difficulties using increasingly accessible online resources for information and services. By utilizing the MarianMT model to create a reliable multilingual machine translation system, this work seeks to address these issues by enhancing the quality of translation for South African languages with limited resources. This study presents a comprehensive approach to multilingual machine translation focused on low-resourced South African languages, utilizing the MarianMT model. Our objective was to enhance translation quality for languages with limited training data, including Afrikaans, Sesotho sa Leboa (Sepedi), isiNdebele, Setswana, IsiXhosa, Xitsonga, siSwati and isiZulu. We conducted extensive experiments, resulting in a BLEU score of 62%, indicating a significant improvement in translation. Our evaluation metrics demonstrated strong performance, with precision at 78%, recall at 81%, F1 score at 79%, and overall accuracy at 82%.

关键词： Measurement Translation Accuracy Training data Neural machine translation Multilingual Reliability Information technology

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Transforming Education with data Mining: Opportunities, Applications, and Challenges

Transforming Education with Data Mining: Opportunities, Appl...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Amey Raut Shital Hajare Department of Computer Science & Engineering Faculty of Engineering & Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Educational data Mining (EDM), a scientific subject that emphasizes employing data analysis to improve instruction and learning, was created because of the increased usage of data in education. Through the application of predictive analytics, clustering, and personalized learning systems EDM helps the educators to understand the student performance, interest and requirement. Identifying problem areas that require attention, allocating resources as efficiently as feasible, and developing learning experiences that are customized to each student's needs are all made possible by the data generated by EDM. Although EDM has the power to change the face of education for the better, there are issues such as data privacy, ethical issues and absence of guidelines that still pose a threat. These challenges may be hailed as future advancements in the fields of artificial intelligence, blockchain and other related technologies could also solve these challenges. With the growth of the digital education EDM is likely to transform the way teaching and learning is done by making it more customized, barrier free and effective in the near future.

关键词： Learning systems Ethics data privacy Technological innovation Shape Education Neural networks Transforms Blockchains Predictive analytics

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Deep Learning-based Semantic Search Techniques for Enhancing Product Matching in E-commerce

Deep Learning-based Semantic Search Techniques for Enhancing...

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Computing and Machine Intelligence (ICMI), International Conference on

作者： Fatima Aamir Raheimeen Sherafgan Tehreem Arbab Akhtar Jamil Fazeel Nadeem Bhatti Alaa Ali Hameed Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Data and Machine Learning Lead Upstart Commerce Islamabad Pakistan Department of Computer Engineering Istinye University Istanbul Turkey

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

Searching is the process of information retrieval utilizing specific criteria or keywords. Integrating search function-alities on e-commerce platforms enables users to efficiently locate exactly what they are searching for through keyword matching. Beyond conventional keyword matching, semantic search involves aligning products with customer queries by capturing the essence of the queries, thereby retrieving semantically related products from the pertinent catalog. Semantic search enhances the e-commerce shopping experience by allowing platforms to tailor responses to user preferences through an in-depth understanding of search intents. Challenges such as morphological variations, spelling errors, and the interpretation of synonyms, antonyms, and hypernyms are addressed through deep learning models de-signed for semantic query-product matching. This study conducts a comparative analysis of various semantic search methodologies and assesses their efficacy, incorporating deep learning strate-gies for query auto-completion and spelling corrections. The evaluation employs sentence transformer models to determine the optimal approach for semantic searching, gauged by nDCG, MRR, and MAP metrics. LSTM, BART, and n-gram models are also examined for auto-completion capabilities. The research analyzes the Amazon Shopping Queries dataset and the Upstart Commerce catalog datasets.

关键词： Deep learning Measurement Training Semantic search Transformers Information retrieval Electronic commerce

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Multi-view aggregation and multi-relation alignment for few-shot fine-grained recognition

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Expert Systems with Applications 2025年 283卷

作者： Chen, Jiale Xu, Feng Lyu, Xin Zeng, Tao Li, Xin Chen, Shangjing College of Computer Science and Software Engineering Hohai University Nanjing210024 China School of Computer Engineering Jiangsu Ocean University Lianyungang222005 China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing211106 China

Few-shot fine-grained recognition (FS-FGR) aims to recognize nuanced categories with a limited number of labeled samples that were not encountered during training. Previous work has made significant progress by enhancing the learning of foreground refined regions and the alignment of consistent semantics. However, the detrimental impact of insufficient background diversity on constructing representative category prototypes has been overlooked. Meanwhile, the alignment of semantically consistent features has been hampered by the reliance on singular metrics, resulting in suboptimal feature extraction. To address the limitations above, a novel framework with multi-view aggregation and multi-relation alignment (MVRA) is proposed. In this framework, we strive to refine category prototypes by generating and consolidating multiple views from limited learnable samples. Specifically, we generate foreground-refined views, pinpointing discriminative regions, and background-obfuscated views, broadening the landscape of background diversity. Further, without relying on the entire prior, a global label assignment module is designed to automatically assign reliable labels to the query set samples. Finally, armed with these credible labels, the multi-relation alignment module harnesses the enriched views and their semantic congruencies, facilitating robust feature extraction. The effectiveness and outstanding performance of MVRA are evaluated through extensive experiments conducted on three fine-grained benchmark datasets. © 2025

关键词： Feature extraction

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Auto Text Correction Using NLP Techniques

Auto Text Correction Using NLP Techniques

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Nalla Srivarsha Gummadi Nithin Shanmugasundaram Hariharan Bibhuti Bhusan Dash Subrata Chowdhury Sudhansu Shekhar Patra Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India School of Computer Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India Dept. of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies(A) Chittoor Andhra Pradesh India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Auto text correction is a fundamental tool in modern Natural Language Processing (NLP) that upgrades user involvement by decreasing typographical and linguistic errors in composed content. This extends leverages progressed NLP procedures to create a strong and proficient auto-correction framework that precisely predicts and corrects content input errors in real-time. The framework utilizes a combination of deep learning models, such as sequence-to-sequence models and transformers, in conjunction with conventional strategies like n-grams and edit distance algorithms. By analyzing the context and structure of input sentences, the model is able to offer relevant rectifications, making it versatile to different dialects and user composing styles. Moreover, we investigate diverse assessment measurements and compare different calculations to optimize the precision and productivity of the correction system. The arrangement has potential applications in word processors, mobile keyboards, and other content input platforms where high accuracy and low latency are crucial.

关键词： Productivity Program processors Keyboards Linguistics Transformers Prediction algorithms Natural language processing Real-time systems Low latency communication Mobile computing

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Practitioners’ Expectations on Log Anomaly Detection

arXiv

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arXiv 2024年

作者： Ma, Xiaoxue Li, Yishu Keung, Jacky Yu, Xiao Zou, Huiqi Yang, Zhen Sarro, Federica Barr, Earl T. Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University Hong Kong Department of Computer Science City University of Hong Kong Hong Kong State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Department of Computer Science Johns Hopkins University Baltimore United States School of Computer Science and Technology Shandong University Shandong China Department of Computer Science University College London London United Kingdom

Log anomaly detection has become a common practice for software engineers to analyze software system behavior. Despite significant research efforts in log anomaly detection over the past decade, it remains unclear what are practitioners’ expectations on log anomaly detection and whether current research meets their needs. To fill this gap, we conduct an empirical study, surveying 312 practitioners from 36 countries about their expectations on log anomaly detection. In particular, we investigate various factors influencing practitioners’ willingness to adopt log anomaly detection tools. We then perform a literature review on log anomaly detection, focusing on publications in premier venues from 2014 to 2024, to compare practitioners’ needs with the current state of research. Based on this comparison, we highlight the directions for researchers to focus on to develop log anomaly detection techniques that better meet practitioners’ expectations. © 2024, CC BY-NC-SA.

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SurgicalPart-SAM: Part-to-Whole Collaborative Prompting for Surgical Instrument Segmentation

arXiv

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arXiv 2023年

作者： Yue, Wenxi Zhang, Jing Hu, Kun Wu, Qiuxia Ge, Zongyuan Xia, Yong Luo, Jiebo Wang, Zhiyong School of Computer Science The University of Sydney Australia School of Software Engineering South China University of Technology China Department of Data Science & AI Monash University Australia School of Computer Science Northwestern Polytechnical University China Department of Computer Science University of Rochester United States

The Segment Anything Model (SAM) exhibits promise in generic object segmentation and offers potential for various applications. Existing methods have applied SAM to surgical instrument segmentation (SIS) by tuning SAM-based frameworks with surgical data. However, they fall short in two crucial aspects: (1) Straightforward model tuning with instrument masks treats each instrument as a single entity, neglecting their complex structures and fine-grained details;and (2) Instrument category-based prompts are not flexible and informative enough to describe instrument structures. To address these problems, in this paper, we investigate text promptable SIS and propose SurgicalPart-SAM (SP-SAM), a novel SAM efficient-tuning approach that explicitly integrates instrument structure knowledge with SAM’s generic knowledge, guided by expert knowledge on instrument part compositions. Specifically, we achieve this by proposing (1) Collaborative Prompts that describe instrument structures via collaborating category-level and part-level texts;(2) Cross-Modal Prompt Encoder that encodes text prompts jointly with visual embeddings into discriminative part-level representations;and (3) Part-to-Whole Adaptive Fusion and Hierarchical Decoding that adaptively fuse the part-level representations into a whole for accurate instrument segmentation in surgical scenarios. Built upon them, SP-SAM acquires a better capability to comprehend surgical instruments in terms of both overall structure and part-level details. Extensive experiments on both the EndoVis2018 and EndoVis2017 datasets demonstrate SP-SAM’s state-of-the-art performance with minimal tunable parameters. The code will be available at https://***/wenxi-yue/SurgicalPart-SAM. Copyright © 2023, The Authors. All rights reserved.

关键词： Surgery

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Potato Leaf Disease Classification Using Deep Learning Approach

Potato Leaf Disease Classification Using Deep Learning Appro...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Sowmiya R Revathi P Sasikala A Jayasundar S Prathep Kumar R Sauvik Deb Department of Artificial Intelligence and Data Science SNS College of Engineering Coimbatore Tamilnadu India Department of Commerce PSGR Krishnammal College for Women Coimbatore Tamilnadu India Department of Computer Science and Engineering Idhaya Engineering college for Women Chinnasalem Tamilnadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

As the fourth most popular staple food in the world, potatoes are among the foods that are extensively consumed. Additionally, the global corona virus pandemic is the leading cause of the sharp rise in potato demand. Moreover, the decrease in the quantity and quality of harvest can be attributed to the prevalence of potato illness. It is crucial to promptly and accurately identify these diseases to prevent further deterioration of plant health. This is where our research comes in. By harnessing the potential of deep learning, we propose a powerful solution that utilizes the VGG16 and VGG19 convolutional neural network models to classify 4 types of potato leaf diseases based on leaf conditions. Not only does this approach provide an accurate classification system, but it also enables early detection of diseases, ensuring the well-being of potato plants and a successful harvest. The average accuracy of 97% in this experiment suggests that the deep neural network technique is feasible.

关键词： Deep learning COVID-19 Accuracy Pandemics Communication systems Convolutional neural networks Diseases

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Towards A Clustering Guided Rule Interpolation for ANFIS Construction

Towards A Clustering Guided Rule Interpolation for ANFIS Con...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Jing Yang Tianhua Chen Lu Chen Jianbin Zhao School of Automation and Software Engineering Shanxi University Taiyuan China Department of Computer Science University of Huddersfield Huddersfield UK Insti. of Big Data Science & Industry Shanxi University Taiyuan China

ISBN: (数字)9798350319545

ISBN: (纸本)9798350319552

How to construct an effective ANFIS (Adaptive Network-based Fuzzy Inference System) with insufficient (sparse) training data is a challenging problem, as the rule base of such an ANFIS model will be sparse. Fuzzy rule interpolation technique enables fuzzy inference to be performed over a sparse rule base, so it is natural to introduce FRI to support the ANFIS construction. In this work, a new clustering guided rule interpolation approach is proposed for the ANFIS construction problem. Different with most existing FRI based ANFIS construction methods that commonly conduct rule interpolation at individual rule level, the proposed method makes the interpolation to be performed on a cluster level. It adopts a clustering strategy to guide the rule selection and rule weights calculation processes, ensuring the rule similarity and diversity at the same time. Particularly, the proposed approach firstly generates a rule dictionary and divides it into several clusters. Following that a cluster guided method is designed for automated selection of relevant rules from each cluster to be included in subsequent interpolation process. Then the weight for each selected rule is calculated by considering both the cluster size and the cluster distance. Experimental results against benchmark regression datasets indicate the effectiveness of the proposed approach.

关键词： Fuzzy logic Interpolation Adaptation models Dictionaries Adaptive systems Training data Benchmark testing

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