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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Yadav, Roshan Kumar Kumar, Divyam Kadam, Kalyani Devi, Seeta Gangarde, Rupali Department of Artificial Intelligence and Machine Learning Pune India Department of Obstetrics and Gynecological Nursing Pune India Department of Computer Science and Engineering Pune India

ISBN: (纸本)9798350383652

Cervical cancer, caused by the skin infection of the Human papillomavirus (HPV), ranked as the fourth most prevalent cancer globally, has profound and sometimes fatal implications for women. With approximately 500,000 new cases reported annually, the disease significantly impacts women's overall well-being. Ongoing efforts by researchers focus on developing a more cost-effective solution than regular HPV testing to manage and control this widespread health concern. Our research included an innovative framework for identifying and classifying stages of cervical cancer by processing the images of the tumours. To optimize the performance of our model, we developed a unique approach for selecting the best image data points by calculating a score, which is defined as the product of focus, sharpness, and resolution. Additionally, our study distinguishes itself by integrating three diverse feature extraction techniques-Local Binary Pattern (LBP), ResNet, and VGGNet-an inclusive strategy not commonly observed in similar projects. We even added deep convolutional architectures and machine learning techniques, including Random Forest, SVM, Decision Tree, XGBoost, a basic Neural Network Classifier, and a Multi-layered Perceptron (MLP). This hybrid approach allowed for an intrinsic comparison, identifying the most effective strategy among these. In contrast to current state-of-the-art methods, our approach yielded around 75% accuracy and 81% AUC score, which is greater than the existing studies on such dataset. © 2024 IEEE.

关键词： Contrastive learning

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UNDERSTANDING TRANSFERABLE REPRESENTATION learning AND ZERO-SHOT TRANSFER IN CLIP 12

UNDERSTANDING TRANSFERABLE REPRESENTATION LEARNING AND ZERO-...

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12th International Conference on learning Representations, ICLR 2024

作者： Chen, Zixiang Deng, Yihe Li, Yuanzhi Gu, Quanquan Department of Computer Science University of California Los Angeles United States Machine Learning Department Carnegie Mellon University Pittsburgh United States

Multi-modal learning has become increasingly popular due to its ability to leverage information from different data sources (e.g., text and images) to improve the model performance. Recently, CLIP has emerged as an effective approach that employs vision-language contrastive pretraining to learn joint image and text representations and exhibits remarkable performance in zero-shot learning and text-guided natural image generation. Despite the huge practical success of CLIP, its theoretical understanding remains elusive. In this paper, we formally study transferrable representation learning underlying CLIP and demonstrate how features from different modalities get aligned. We also analyze its zero-shot transfer performance on the downstream tasks. Inspired by our analysis, we propose a new CLIP-type approach, which achieves better performance than CLIP and other state-of-the-art methods on benchmark datasets. © 2024 12th International Conference on learning Representations, ICLR 2024. All rights reserved.

关键词： Zero-shot learning

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Deep neural network-based tools for hotel marketing activities of online travel agents

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Multimedia Tools and Applications 2025年 1-24页

作者： Keles, Ali Keles, Ayturk Keles, Mustafa Berk Mutlu, Behçet Department of Computer Education and Instructional Technology Faculty of Education Agri Ibrahim Cecen University Agri04100 Turkey Machine Learning Engineer *** Istanbul Turkey Director of Technology R&D Center *** Istanbul Turkey Department of Computer Engineering Gebze Technical University Kocaeli Turkey

Vacationers always want to choose the hotel that best meets their needs and expectations. For this, they typically use online travel agents (OTAs). OTAs must analyze millions of images from thousands of hotels quickly and classify them accurately to best meet demands. This is a highly complex and challenging task. OTAs also have to choose a default image to represent best way each hotel in order to best market hotels to vacationers and encourage click behavior. This is another challenge to overcome. The biggest problem in classifying millions of images from different areas of hotels is that these areas often have similar objects and environments. For this reason, fewer categories have been used in deep learning (DL) studies conducted so far. In this study, two deep neural networks (DNN) have been developed to accomplish these challenging tasks. The first network is the "hotel image classification network" (HICNet), which classifies images of different areas of hotels into 11 categories. In a multi-class classification task that includes closely related categories such as bar, food, and lobby, the HICNet network achieved very good results, reaching an average of 79% precision, sensitivity, and F1-score. The second network is the "hotel default image network" (HDINet), which enables the determination of a default image for each hotel on the OTA page. HDINet can accurately identify the default image of hotels with an average of 92% precision, sensitivity, and F1-score. These networks have been integrated into the OTA system named *** and are currently in active use. © The Author(s) 2025.

关键词： Hotels

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Decoding Bias in #Climatechange Tweets: A Neural Language Style Transfer Approach

Decoding Bias in #Climatechange Tweets: A Neural Language St...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： George, Elizabeth Leah Parthasarathy, Subashini Avinashilingam Institute for Home Science and Higher Education for Women Department of Computer Science Coimbatore India Avinashilingam Institute for Home Science and Higher Education for Women Centre for Machine Learning and Intelligence Department of Computer Science Coimbatore India

ISBN: (纸本)9798331517953

In response to the increase of biased and misleading opinions on social media regarding climate change, we present the Neural Language Style Transfer Bias (NLST Bias) framework - an AI-driven solution to identify and mitigate bias in climate-related tweets. Our approach analyses a curated dataset of climate change tweets collected from Twitter, leveraging spaCy for text pre-processing and BERT-based sentence embeddings to extract semantic features. Evaluation metrics with classifier models showed significant improvements, with CNN-LSTM achieving the highest F1 score of 78%. Most classifiers show marked improvements across accuracy, precision, recall, and F1 score with NLST, highlighting its effectiveness on this dataset. NLST Bias employs DistilBERT to classify tweets as biased or unbiased, with the Named Entity Recognition (NER) model trained on RobERTa transformers to identify word-level bias indicators. The framework's core component is a transformer-based style transfer model (e.g., GPT-2) that modifies the stylistic tone of biased text while preserving its semantic content. This transfer uses a style-specific decoder within the transformer architecture, ensuring the generated text maintains grammatical accuracy and contextual relevance. The resulting output exhibits tone, phrasing, and word choice adjustments to foster a more neutral style. Results indicate a significant reduction in bias without compromising text fluency or naturalness, verified through statistical metrics and human evaluations. Furthermore, Explainable AI techniques are integrated to enhance transparency in model decisions. Our findings underscore NLST Bias's efficacy in countering climate misinformation and promoting balanced discussions on social media, highlighting its potential as a tool for responsible AI in digital discourse. © 2024 IEEE.

关键词： Tweets

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Recent trends in computational intelligence for educational big data analysis

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Iran Journal of computer science 2024年第1期7卷 103-129页

作者： Ikegwu, Anayo Chukwu Nweke, Henry Friday Anikwe, Chioma Virginia Computer Science Department Alex Ekwueme Federal University Ndufu-Alike Ikwo Ebonyi Abakaliki Nigeria Centre for Research in Machine Learning Artificial Intelligence and Network Systems Computer Science Department Ebonyi State University Abakaliki Nigeria

Educational big data analytics and computational intelligence have transformed our understanding of learning ability and computing power, catalyzing the emergence of Education 4.0. However, educators and researchers still struggle to identify appropriate methods to analyze the diverse data generated within educational environments. The complexity and uncertainty inherent in heterogeneous and homogeneous data often compound these challenges. This study aims to explore the potential applications of computational intelligence methods to support educational big data analysis. We begin by discussing the processes involved in educational big data analytics (EDA), including data collection, data preprocessing, feature extraction, modeling, and evaluation. We then provided an extensive review of computational intelligence and its methods, including artificial intelligence approaches, machine learning methods, deep learning methods, meta-heuristic optimization approaches, ensemble techniques, and the Markov model, as applied to educational big data analysis. Furthermore, we discussed novel application areas for computational intelligence in education, including predicting academic performance, social network analysis, detecting undesirable student behaviors, adaptive curriculum sequencing and personalization, courseware development, and decision support systems. We also mapped various computational intelligence methods to these novel application areas. Despite the progress made in educational big data analytics implementation, challenging research areas still require further investigation. These research areas include enhanced academic performance prediction, data-driven intelligent tutoring systems, adversarial machine learning, student engagement, personalized learning, and more. In this paper, we briefly discussed these ten important research directions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Artificial intelligence Big data analytics Computational intelligence Deep learning Educational data mining machine learning

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Probabilistic Artificial Intelligence

arXiv

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arXiv 2025年

作者： Krause, Andreas Hübotter, Jonas Institute for Machine Learning Department of Computer Science

Artificial intelligence commonly refers to the science and engineering ofartificial systems that can carry out tasks generally associated with requiringaspects of human intelligence, such as playing games, translating languages,and driving cars. In recent years, there have been exciting advances inlearning-based, data-driven approaches towards AI, and machine learning anddeep learning have enabled computer systems to perceive the world inunprecedented ways. Reinforcement learning has enabled breakthroughs in complexgames such as Go and challenging robotics tasks such as quadrupedal locomotion. A key aspect of intelligence is to not only make predictions, but reasonabout the uncertainty in these predictions, and to consider this uncertaintywhen making decisions. This is what this manuscript on "ProbabilisticArtificial Intelligence" is about. The first part covers probabilisticapproaches to machine learning. We discuss the differentiation between"epistemic" uncertainty due to lack of data and "aleatoric" uncertainty, whichis irreducible and stems, e.g., from noisy observations and outcomes. Wediscuss concrete approaches towards probabilistic inference and modernapproaches to efficient approximate inference. The second part of the manuscript is about taking uncertainty into account insequential decision tasks. We consider active learning and Bayesianoptimization - approaches that collect data by proposing experiments that areinformative for reducing the epistemic uncertainty. We then considerreinforcement learning and modern deep RL approaches that use neural networkfunction approximation. We close by discussing modern approaches in model-basedRL, which harness epistemic and aleatoric uncertainty to guide exploration,while also reasoning about safety. © 2025, CC BY-NC-SA.

关键词： Deep neural networks

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Automated Quality Assessment Using Appearance-Based Simulations and Hippocampus Segmentation on Low-Field Paediatric Brain MR Images 1st

Automated Quality Assessment Using Appearance-Based Simulati...

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1st MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2024, held in Conjunction with Medical Image Computing and computer Assisted Intervention Conference, MICCAI 2024

作者： Sundaresan, Vaanathi Dinsdale, Nicola K Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India Oxford Machine Learning in NeuroImaging Lab Department of Computer Science University of Oxford Oxford United Kingdom

ISBN: (纸本)9783031830105

Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many factors, including the lack of automated image analysis tools, especially in Low and Middle Income Countries from the lack of high field MR images available. Low-field systems are being increasingly explored in these countries, and, therefore, there is a need to develop automated image analysis tools for these images. In this work, as a preliminary step, we consider two tasks: 1) automated quality assurance and 2) hippocampal segmentation, where we compare multiple approaches. For the automated quality assurance task a DenseNet combined with appearance-based transformations for synthesising artefacts produced the best performance, with a weighted accuracy of 82.3%, thus ranking in the 1st place in the LISA2024 Challenge. For the segmentation task, registration of an average atlas performed the best, with a final Dice score of 0.61. Our results show that although the images can provide understanding of large scale pathologies and gross scale anatomical development, there still remain barriers for their use for more granular analyses. © The Author(s) 2025.

关键词： Magnetic resonance imaging

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Enhanced Steganalysis for Color Images Using Curvelet Features and Support Vector machine

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computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan Machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

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An Analysis and Detection of Depression from Textual Data using Deep learning Methods 2

An Analysis and Detection of Depression from Textual Data us...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Chaithra, I.V. Sunitha, M.R. Department of Computer Science Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India Department of Artificial Intelligence and Machine Learning Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

Depression is a prevalent sickness, spreading worldwide with potentially serious implications. Timely recognition of emotional responses plays a pivotal function at present. Mental ill health is highly risky, stirring more than 300 million population. Because of this reason more focus of research is on this area. A statement by American Psychiatric Association "6.715 adults (6.7%) is get affected in any given year. One-sixth of people(16.6%) will experience depression in their life." Studies said that Women are in greater proportion to likely experience depression when compared to men. One among three women will share a significant depressive symptoms in their lifetime. Written text reflects its impact on language usage. The count of afflicted individuals can be lessened, using an advanced machine learning techniques and the availability of test data related to depression. This paper proposes different preprocessing techniques. Feature extraction techniques such as BoW(Bag of Words), Word2Vec, Glove, N-Gram, LIWC and LDA. Classification techniques such as Naive Bayes, SVM, LR, Adaboost and RF. © 2024 IEEE.

关键词： Deep learning

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A Vision Based Approach for Real Time Detection of Proximity Hazards around Open Wells: An Application in Open Well Safety Monitoring

A Vision Based Approach for Real Time Detection of Proximity...

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2024 IEEE Conference on Engineering Informatics, ICEI 2024

作者： Naik, Aravind Bhat, Sandeep Naik, Daya Srinivas Institute of Technology Department of Computer Science and Engg Mangaluru India Srinivas Institute of Technology Department of Artificial Intelligence and Machine Learning Mangaluru India

ISBN: (纸本)9798331505776

Open wells are a major hazard in India's rural and urban settings, especially for youngsters and unwary adults. The absence of safety barriers surrounding wells frequently causes mishaps, which can occasionally be fatal. This study suggests a brand-new, reasonably priced, vision-based solution that uses a CCTV camera to identify people approaching a well and sound an alarm when they get too close. The system draws boundary boxes and determines the distance between the well and the person using object detection methods to precisely detect and classify both. Accidents may be avoided if an automated alert is set off if the distance between the person and the well drops below a certain level. This method is trained using custom YOLOv8 models for well detection and a pre trained model for person detection. This paper emphasizes the system's importance in the context of countries like India, where safety infrastructure around wells is often insufficient, and accidental falls are a common hazard. © 2024 IEEE.

关键词： Accidents

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