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Optimised EfficientNet for multi-class skin cancer diagnosis: integrating chaotic grey wolf algorithms

Current science 2025年第7期128卷 728-736页

作者： Rama, P. Angayarkanni, V. Theresa, W. Gracy Pandiaraj, A. Department of Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603 203 India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai600 123 India

The detection of skin cancer holds paramount importance worldwide due to its impact on global health. While deep convolutional neural networks (DCNNs) have shown potential in this domain, current approaches often struggle with fine-grained variability in skin lesion features, imbalanced datasets and inadequate augmentation. The objectives of our study encompass building a model that not only enhances accuracy but also reduces training time and cost, improves dimensionality reduction during classification and segmentation, and is evaluated on larger datasets for robustness. We enhance the learning process using an improved nature-inspired optimisation algorithm tailored specifically for skin cancer classification tasks. This algorithm optimises model parameters to maximise classification accuracy while minimising computational overhead. Our proposed architecture leverages a gold standard dermatological image dataset meticulously curated and validated by experts. This dataset spans eight distinct classes, including actinic keratosis, basal cell carcinoma, melanoma, nevus, and others, ensuring a diverse representation of skin conditions. The balanced class distribution in the training and test sets enables fair evaluation and robustness assessment of our deep learning models. The developed algorithm showcases novelty in its approach, contributing to advancements in early skin cancer detection technologies. This study introduces an optimised deep learning model designed for the early detection of skin cancer, achieving a notable accuracy of 97.48%. Our study aims to contribute significantly to the medical field by providing a reliable and efficient tool for dermatologists and healthcare practitioners to assist in diagnosing skin conditions accurately and promptly, ultimately improving patient outcomes and healthcare efficiency. © (2025), (Indian Academy of sciences). All rights reserved.

关键词： Convolutional neural networks

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Evolution of Consensus Algorithms in Blockchain Technology

Evolution of Consensus Algorithms in Blockchain Technology

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2023 International Conference on Intelligent Systems for Communication, IoT and Security, ICISCoIS 2023

作者： Harshini Poojaa, K. Ganesh Kumar, S. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu India

ISBN: (纸本)9798350335835

Blockchain being a decentralised, distributed ledger, has attracted industries to implement the solutions on blockchain. The transactions are agreed in a deterministic manner because of the availability of consensus algorithms. The state of the machine is concluded based on the agreement generated by the consensus algorithm. The performance of the blockchain system can be increased to the order of magnitude when the right consensus is used. In this paper, the limitations and performance of various consensus algorithms have been reviewed along with the characteristics of the same. In addition to that, this paper compares the consensus algorithms based on various parameters such as speed, double-spending, 51% attack and many more. © 2023 IEEE.

关键词： Blockchain

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Robustness of Explainable AI Algorithms for Disease Biomarker Discovery from Functional Connectivity datasets

Robustness of Explainable AI Algorithms for Disease Biomarke...

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2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Girish, Deepank Chan, Yi Hao Gupta, Sukrit Xia, Jing Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Singapore Indian Institute of Technology Department of Computer Science and Engineering Ropar India

ISBN: (纸本)9798350351552

Machine learning (ML) models have been used in functional neuroimaging for wide-ranging tasks, ranging from disease diagnosis to disease prognosis. There have been successive functional connectivity-based ML studies focused on improving model performances for disease detection. An increasing number of such studies use their trained models to detect and evaluate salient features that could be potential biomarkers of these neuro-logical conditions. The evaluation of these salient features is often qualitative and limited to cross-referencing existing literature for similar findings. In this study, we present objective quantitative metrics to evaluate the robustness of these salient features. Building upon existing generic evaluation metrics, we propose metrics that capture topological properties known to be characteristic of brain functional connectomes. Using existing and newly proposed measures on a set of baselines and state-of-the-art graph neural networks (GNN) models, we found that when GNNExplainer is used with models that incorporate attention, the scores produced are relatively more robust than other combinations. On datasets of patients with Autism Spectrum Disorder (ASD) or Attention-deficit Hyperactivity Disorder (ADHD), our proposed metrics highlighted that salient features identified in both disorders are highly involved in functional specialization, while salient ASD features expressed stronger functional integration than ADHD. We package these existing and novel metrics together in the RE-CONFIRM framework that holds promise to set the foundations for the quantitative evaluation of salient features detected by future studies. © 2024 IEEE.

关键词： Graph neural networks

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data-Faithful Feature Attribution: Mitigating Unobservable Confounders via Instrumental Variables 38

Data-Faithful Feature Attribution: Mitigating Unobservable C...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Sun, Qiheng Xia, Haocheng Liu, Jinfei Zhejiang University China Institute of Blockchain and Data Security China Siebel School of Computing and Data Science University of Illinois Urbana-Champaign United States

The state-of-the-art feature attribution methods often neglect the influence of unobservable confounders, posing a risk of misinterpretation, especially when it is crucial for the interpretation to remain faithful to the data. To counteract this, we propose a new approach, data-faithful feature attribution, which trains a confounder-free model using instrumental variables. The cluttered effects of unobservable confounders in a model trained as such are decoupled from input features, thereby aligning the output of the model with the contribution of input features to the target feature in the data generation. Furthermore, feature attribution results produced by our method are more robust when focusing on attributions from the perspective of data generation. Our experiments on both synthetic and real-world datasets demonstrate the effectiveness of our approaches. © 2024 Neural information processing systems foundation. All rights reserved.

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From Text to Visuals: Advancements in Depression Prediction Using AI and Machine Learning Techniques

From Text to Visuals: Advancements in Depression Prediction ...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Annapoorani, S. Saravanan, P. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulatur India

ISBN: (纸本)9798350305708

Social lifestyle and COVID pandemic situation to produce a direct impact on mental health. Depression remains a primary cause of disability worldwide and contributes significantly to the global health burden. It can typically lead to suicidal ideation and have a negative impact on anyone's professional and personal life. Artificial intelligence, machine learning, and deep learning techniques can forecast depression in a timely manner utilizing a variety of resources. Different kinds of sources, such as surveys, social networking sites, video images, and speech, can be used to accurately identify depression without the assistance of a psychiatrist. People on social networking sites express their feelings through appropriate text, which may be examined using machine learning algorithms. Through visual and speech data, deep learning approaches properly provide remarkable accuracy in predicting depression. This survey study is useful in identifying depression accuracy in advanced ways utilizing diverse resources. © 2023 IEEE.

关键词： Classification Algorithm Deep Learning Depression Ensemble Method Machine Learning

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Creating a Blueprint for Success in First-Year computing

Creating a Blueprint for Success in First-Year Computing

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2023 ASEE Annual Conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023

作者： Kreimendahl, Frank Schuster, Micah Suresh-Menon, Durga School of Computing and Data Science Wentworth Institute of Technology BostonMA United States

First-year computing is the most important pathway to undergraduate degrees and student success in computing, data science, engineering, and related fields. While there is an abundance of research in the area, we still lack retention of students in first-year computing. This is due to many factors including, but not limited to, 1) student math preparation and readiness from high school, 2) adjunct faculty teaching classes where no common coursework is employed, 3) faculty teaching the same courses in different ways, creating inconsistency in material covered, and 4) lack of tutoring for students who are struggling, behind, or do not understand the material. This paper presents task force results from a collaboration between faculty and academic support specialists. The paper focuses on two high-impact areas of improvement: standardizing curricula and building support scaffolding outside the classroom. The results, a comprehensive course blueprint, include core resources for a first-semester computing course and recommendations for identification and support of struggling students. © American Society for Engineering Education, 2023.

关键词： Students

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Enhanced study on Deep learning model for kidney segmentation using DCE-MRI

Enhanced study on Deep learning model for kidney segmentatio...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Nagarajan, S. Ramprasath, M. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chengalpattu India

ISBN: (纸本)9798350305708

Medical imaging is capturing pictures of bodily components for diagnostic or research reasons. Because of advancements in image-handling techniques, which include picture recognition, examination, and upgrading, clinical imaging is expanding swiftly. Picture division is a vital subject in picture handling and PC vision with applications, for example, scene grasping, clinical picture examination, mechanical discernment, video observation, expanded reality, and picture pressure, among numerous others. Numerous picture segmentation methods have been created and are available in the literature. There has been a lot of work done to develop techniques for segmenting pictures using deep-learning models since they have proved effective in various vision applications. Deep learning has turned into a functioning exploration point in the field of clinical picture examination. Compared to the laborious and lengthy conventional technique, deep learning (DL) automatic detection algorithms can speed up diagnostics, increase test accuracy, lower expenses, and lessen the workload on the radiologist. Kidney segmentation is an integral part of any non-intrusive computer-assisted diagnostic (CAD) system for rapidly recognising severe renal repudiation because of the intensity of inhomogeneity brought on by errors in the image capture process, efficient kidney segmentation is challenging in evaluating and therapeutic planning of kidney-related disorders. This article addresses recent developments in DL-based kidney tumor segmentation systems. We highlight their fundamental components and numerous approaches as we discuss the main medical image kinds, segmentation methods, and evaluation standards for segmentation outcomes in kidney tumor++ segmentation. © 2023 IEEE.

关键词： Automatic Detection algorithms Computer-Assisted diagnostic system Deep Learning Kidney tumor segmentation Vision applications

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CareerBoost: A Hybrid RAG-NLP Job Recommendation Framework 8

CareerBoost: A Hybrid RAG-NLP Job Recommendation Framework

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Deshmukh, Sanskruti Bajaj, Aaryaman Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science Pune India Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India

ISBN: (纸本)9798350376425

The proposed system presents a novel approach to job recommendation and resume enhancement, leveraging advanced Natural Language Processing (NLP) techniques and Retrieval-Augmented Generation (RAG). We introduce a comprehensive system that combines semantic search, FAISS-based similarity matching, and a fine-tuned GPT-2 model to provide personalized job recommendations and actionable resume improvement suggestions. The evaluation, conducted through offline metrics and online A/B testing with 5,000 users, demonstrates significant improvements over traditional keyword-matching approaches. The RAG-based system achieved a 25.8% increase in recommendation relevance (Precision@10), a 45.7% increase in user engagement, and a 41.2% increase in job application rates. Furthermore, users who applied the system's resume enhancement suggestions saw a 9.0% improvement in subsequent job matches. These results highlight the potential of AI-driven systems to revolutionize online recruitment and career development, while also emphasizing the need for ongoing consideration of ethical implications and fairness in AI-assisted hiring processes. © 2024 IEEE.

关键词： Semantics

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A Progressive Skip Reasoning Fusion Method for Multi-Modal Classification 24

A Progressive Skip Reasoning Fusion Method for Multi-Modal C...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Qian Liang, Xinyan Qian, Yuhua Cui, Zhihua Wen, Jie Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China Institute of Big Data Science and Industry Shanxi University Taiyuan China

ISBN: (纸本)9798400706868

In multi-modal classification tasks, a good fusion algorithm can effectively integrate and process multi-modal data, thereby significantly improving its performance. Researchers often focus on the design of complex fusion operators and have proposed numerous fusion operators, while paying less attention to the design of feature fusion usage, specifically how features should be fused to better facilitate multi-modal classification tasks. In this article, we propose a progressive skip reasoning fusion network (PSRFN) to make some attempts to address this issue. Firstly, unlike most existing multi-modal fusion methods that only use one fusion operator in a single stage to fuse all view features, PSRFN utilizes the progressive skip reasoning (PSR) block to fuse all views with a fusion operator at each layer. Specifically, each PSR block utilizes all view features and the fused features from the previous layer to jointly obtain the fused features for the current layer. Secondly, each PSR block utilizes a dual-weighted fusion strategy with learnable parameters to adaptively allocate weights during the fusion process. The first level of weighting assigns weights to each view feature, while the second level assigns weights to the fused features from the previous layer and the fused features obtained from the first level of weighting in the current layer. This strategy ensures that the PSR block can dynamically adjust the weights based on the actual contribution of features. Finally, to enable the model to fully utilize feature information from different levels for feature fusion, the skip connections are adopted between PSR blocks. Extensive experiment results on six real multi-modal datasets show that a better usage for fusion operator is indeed able to improve performance. © 2024 ACM.

关键词： Classification (of information)

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Counseling Responses for Mental Health Forum Questions with Early Maladaptive Schema Prediction 27

Counseling Responses for Mental Health Forum Questions with ...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Gollapalli, Sujatha Das Ang, Beng Heng Du, Mingzhe Ng, See-Kiong Institute of Data Science National University of Singapore Singapore Integrative Sciences and Engineering Programme National University of Singapore Singapore College of Computing and Data Science Nanyang Technological University China

ISBN: (纸本)9781643685489

State-of-the-art Large Language Models (LLMs) have shown remarkable capabilities for general Question Answering (QA) tasks. However, their practical use for answering mental health questions has been limited due to the missing link between LLM-generated answer responses and well-established theories and guiding principles from Psychology and Counseling. We present a first step in this direction with STeer, an AI-based method that supports Schema Therapy-enabled responses for mental health questions on community QA forums. STeer uses Early Maladaptive Schemas (EMSs), a fundamental concept from Schema Therapy that characterizes "self-defeating, unhealthy patterns of thought and behavior" in individuals, to effectively prompt state-of-the-art LLMs to generate specific, theoretically-grounded, empathetic counseling responses to mental health questions. We present EMSRank, a novel method based on the Personalized PageRank algorithm, to automatically predict the EMSs from mental health forum question texts. We show that EMSRank is computationally scalable and can be further combined with textual entailment to obtain high precision, explainable EMS labels for mental health forum questions. To address the current lack of annotated datasets, we also leveraged on EMSRank to create a first-of-its-kind, large dataset of about 23K EMS-annotated mental health questions from three diverse, currently operating, peer-support community forums for mental health. With the global rise in mental health issues, our work is a timely step towards enabling the use of AI-based assistive tools for counseling support on mental health community forums. © 2024 The Authors.

关键词： Electronic health record

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