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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Jasmine, R.Anita Ramla, M. Ramesh, M. Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai Kattankulathur India Srm Institute of Science and Technology Department of Computer Applications Chennai Kattankulathur India Srm Institute of Science and Technology Department of Computer Science Chennai Kattankulathur India

ISBN: (纸本)9798350369083

Diabetic Retinopathy (DR) is widely recognized as the primary cause of visual impairment worldwide. Early intervention is crucial in preventing irreversible vision loss. Ophthalmologists conventionally utilize fundus cameras to capture retinal images and detect indications of diabetic retinopathy, thereby facilitating prompt diagnosis. The progress made in computational intelligence has significantly enhanced our ability to identify, predict, and categorize DR. The focus of this research is to present a proficient framework for the DR grading. The framework proposed employs Laplacian filters to reduce undesirable image distortions and enhance the overall quality of the images. The methodology involves the utilization of transfer learning through the pre-existing VGG19 Convolutional Neural Network architecture to pull out features from diverse pooling layers. The amalgamation of extracted features from multiple layers results in the formation of feature vectors. These vectors are utilized as inputs for training various machine learning algorithms, with the objective of identifying the most effective model for evaluating the severity of DR. Significantly, the research provides a rationale for its forecasts, guaranteeing the transparency of the algorithm and comprehensibility of the model, thereby validating its reliability. The model's exceptional precision renders it a promising candidate for broad implementation in clinical identification and prediction of DR, potentially augmenting global patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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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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Advancing Cervical Cancer Risk Stratification via Ensemble Learning Models Integrated with SHAP-Based Interpretability Methods

Advancing Cervical Cancer Risk Stratification via Ensemble L...

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2024 IEEE International Conference on Computing, ICOCO 2024

作者： Rakin, Abdullah Al Sharif, Kazi Shaharair Nayyem, Mohammad Navid Raju, Md Azad Hossain Arafin, Rudmila Hossain, Md Zakir Department of Computer Science University of South Dakota SD United States Department of Computer Science BRAC University Dhaka Bangladesh Master’s in Data Science Grand Canyon University AZ United States

ISBN: (纸本)9798331530303

Cervical cancer remains a leading cause of cancer-related mortality among women globally, particularly in regions with limited access to regular screening. Early detection improves prognosis, yet traditional methods such as Pap smears and HPV testing are often unavailable in low-resource settings. In this work, we present a novel machine learning framework for cervical cancer risk prediction, leveraging a Stacking ensemble of Gradient Boosting (GB), Random Forest (RF), XGBoost (XGB), and Support Vector Machines (SVM). To address class imbalance in the dataset, we apply the Synthetic Minority Over-sampling Technique (SMOTE), while Recursive Feature Elimination (RFE) identifies the most relevant clinical and demographic features. Model interpretability is ensured through SHapley Additive exPlanations (SHAP), providing insights into feature contributions. Our approach achieves a state-of-the-art accuracy of 99.00%, outperforming individual models and demonstrating the effectiveness of ensemble learning for early diagnosis. This work presents a scalable, interpretable, and clinically viable solution for improving cervical cancer screening in resource-constrained environments, setting a new benchmark in predictive modeling for medical diagnostics. ©2024 IEEE.

关键词： Support vector machines

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Comparative Analysis of Spatial Human Mobility Parameters in 15 Most Populous U.S. Metropolitan Statistical Areas

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IAENG International Journal of Applied Mathematics 2024年第6期54卷 997-1012页

作者： Matloub, Zaid Kostanic, Ivica Senior Data Engineer at Better World Analytics MelbourneFL United States Department of Electrical Engineering and Computer Science Florida Institute of Technology MelbourneFL United States

This research paper presents the results of two studies investigating human mobility patterns in the 15 largest Metropolitan Statistical Areas (MSAs) in the United States. It studied 14 daily mobility parameters aggregated at the MSA level, derived from four primary mobility parameters: Number of Visited Locations (N_LOC), Number of Unique Visited Locations (N_ULOC), Radius of Gyration (R_GYR), and Distance Traveled (D_TRAV) over a 30-day period. The first study was conducted on data from two large MSAs, one coastal and one inland (Boston and Atlanta, respectively). The aim was to examine associations between daily values of mobility parameters aggregated at the MSA level and identify those carrying similar or identical information. Results of factor analysis showed that these could be adequately described by two independent factors, pointing to one or two of the mobility parameters as sufficient to represent the whole set in analyses based on associations. These could either be D_TRAV, as it had high loadings on both factors, or N_LOC and R_GYR due to their high loadings on the two extracted factors. The second study was conducted on daily mobility datasets from the 15 MSAs. The aim was to compare daily mobility patterns of these MSAs and group them based on their mobility pattern similarities. Factor analysis of the aggregated mean daily distances (D_TRAV) across different MSAs over the studied period classified them into two distinct groups: one predominantly composed of inland MSAs and the other primarily of coastal MSAs. Strong weekly cycle trends emerged in these groups. Specifically, individuals from the inland MSA group tended to travel the furthest on Fridays and the least on Sundays, whereas those from the coastal MSA group traveled the most on Saturdays and the least on Mondays. This weekly pattern was robust, with 7-day lag autocorrelations of mean daily parameter values ranging between 0.81 to 0.99, excluding the mean daily N_LOC. These findings offer a

关键词： Big data

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MedEx: Enhancing Medical Question-Answering with First-Order Logic based Reasoning and Knowledge Injection 31

MedEx: Enhancing Medical Question-Answering with First-Order...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zafar, Aizan Mishra, Kshitij Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761964

In medical question-answering, traditional knowledge triples often fail due to superfluous data and their inability to capture complex relationships between symptoms and treatments across diseases. This limits models' ability to provide accurate, contextually relevant responses. To overcome this, we introduce MedEx, which employs First-Order Logic (FOL)-based reasoning to model intricate relationships between diseases and treatments. We construct FOL-based triplets that encode the interplay of symptoms, diseases, and treatments, capturing not only surface-level data but also the logical constraints of the medical domain. MedEx encodes the discourse (questions and context) using a transformer-based unit, enhancing context comprehension. These encodings are processed by a Knowledge Injection Cell that integrates knowledge graph triples via a Graph Attention Network. The Logic Fusion Cell then combines medical-specific logical rule triples (e.g., co-occurrence, causation, diagnosis) with knowledge triples and extracts answers through a feed-forward layer. Our analysis demonstrates MedEx's effectiveness and generalization across medical question-answering tasks. By merging logical reasoning with knowledge, MedEx provides precise medical answers and adapts its logical rules based on training data nuances. © 2025 Association for Computational Linguistics.

关键词： Question answering

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Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification

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computers, Materials & Continua 2023年第4期75卷 149-164页

作者： K.Kalyani Sara A Althubiti Mohammed Altaf Ahmed ELaxmi Lydia Seifedine Kadry Neunggyu Han Yunyoung Nam Department of Computer Science Dr.Nalli Kuppusamy Arts College(Affiliated to Bharathidasan UniversityTiruchirappalli)Thanjavur613003India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Lepartment of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea

Melanoma is a skin disease with high mortality rate while earlydiagnoses of the disease can increase the survival chances of patients. Itis challenging to automatically diagnose melanoma from dermoscopic skinsamples. computer-Aided Diagnostic (CAD) tool saves time and effort indiagnosing melanoma compared to existing medical approaches. In this background,there is a need exists to design an automated classification modelfor melanoma that can utilize deep and rich feature datasets of an imagefor disease classification. The current study develops an Intelligent ArithmeticOptimization with Ensemble Deep Transfer Learning Based MelanomaClassification (IAOEDTT-MC) model. The proposed IAOEDTT-MC modelfocuses on identification and classification of melanoma from dermoscopicimages. To accomplish this, IAOEDTT-MC model applies image preprocessingat the initial stage in which Gabor Filtering (GF) technique is *** addition, U-Net segmentation approach is employed to segment the lesionregions in dermoscopic images. Besides, an ensemble of DL models includingResNet50 and ElasticNet models is applied in this study. Moreover, AOalgorithm with Gated Recurrent Unit (GRU) method is utilized for identificationand classification of melanoma. The proposed IAOEDTT-MC methodwas experimentally validated with the help of benchmark datasets and theproposed model attained maximum accuracy of 92.09% on ISIC 2017 dataset.

关键词： Skin cancer deep learning melanoma classification dermoscopy computer aided diagnosis

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Enhancing Urdu Intrinsic Plagiarism Detection Through Stylometry Features and Machine Learning 25

Enhancing Urdu Intrinsic Plagiarism Detection Through Stylom...

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25th International Multi Topic Conference, INMIC 2023

作者： Manzoor, Muhammad Faraz Farooq, Muhammad Shoaib Haseeb, Muhammad Farooq, Uzma Abid, Adnan Saeed, Ahmer University of Management and Technology Department of Computer Science Lahore Pakistan University of the Punjab Department of Data Science Lahore Pakistan Aircod Technologies Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350317701

The creation of digital content and the easy accessibility of information have led to a surge in academic and textual plagiarism. Plagiarism detection in multiple languages is essential to maintain the integrity of academic and literary works. In the context of the Urdu language, there is a growing need for effective plagiarism detection methods that are tailored to its unique linguistic characteristics. Existing Urdu plagiarism detection tools often rely on external sources or lack robustness in handling intrinsic forms of plagiarism, where the copied content is slightly modified or paraphrased. This research aims to bridge this gap by developing an intrinsic plagiarism detection system for the Urdu language, using a combination of machine learning, ensemble learning and Multi-Layer Perceptron (MLP). Furthermore, to train and evaluate our plagiarism detection models, we manually curate a corpus comprising a substantial collection of 1807 documents in Urdu. This corpus forms the foundation of our research, enabling us to develop and fine-tune our detection algorithms to effectively identify instances of intrinsic plagiarism in Urdu text. To comprehensively assess the unique stylistic fingerprints of documents, we employ a diverse set of word based stylometry features. This multifaceted approach enhances our ability to pinpoint instances of plagiarism in a robust manner. This research contributes to the ongoing efforts to combat plagiarism and uphold the integrity of written content, particularly in the context of the Urdu language, while also showcasing the effectiveness of different word based stylometry features in addressing this critical issue. © 2023 IEEE.

关键词： Machine learning

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A cloud-based architecture for explainable Big data analytics using self-structuring Artificial Intelligence

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Discover Artificial Intelligence 2024年第1期4卷 33页

作者： Mills, Nishan Issadeen, Zafar Matharaarachchi, Amali Bandaragoda, Tharindu De Silva, Daswin Jennings, Andrew Manic, Milos Centre for Data Analytics and Cognition La Trobe University MelbourneVIC3086 Australia Department of Computer Science Virginia Commonwealth University RichmondVA23284 United States

Big data is steadily expanding beyond the boundaries of its foundational constructs of three primary Vs, Volume, Velocity and Variety, and two secondary Vs, Veracity and Value. The advent of 5G networks, Edge computing and IoT technologies has transformed Big data into this modern context. With these new manifestations of Big data, the focus is not only on the data itself but on the context that it applies to its immediate environment as well as the human and societal perception of this context. It is increasingly challenging for conventional AI algorithms to process and transform this data, analyse and visualise a broad spectrum of insights, and then formulate the explainability of such insights in terms of bias, transparency, safety, ethics, and causality. Self-structuring Artificial Intelligence (SSAI) addresses the limitations of conventional AI by adapting to the inherent structure of the data, incrementally learning and abstracting from this structure. SSAI has not been investigated in a cloud-based setting for generating explainable insights from these new types of Big data. In this paper we propose a cloud-based architecture for explainable Big data analytics using SSAI in highly-connected 5G and Edge computing environments. The proposed architecture is empirically evaluated on a commercial scale Big data use case of Smart Grid for Smart Cities. The results of these experiments confirm the functionality and effectiveness of the proposed architecture. © The Author(s) 2024.

关键词： data Analytics

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Comparative Analysis of Hate Speech Detection Using Various Vectorization and Classification Techniques 2

Comparative Analysis of Hate Speech Detection Using Various ...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Tali, Sophia Noguera, Daniel Gargees, Rasha S. Central Michigan University Department of Data Science Actuarial Science and Statistics MI United States Central Michigan University Department of Computer Science MI United States

ISBN: (纸本)9798331529529

Hate speech is a prominent, growing problem within our society, and this problem and its effects have only increased with time. According to the Center for Technology and Society, 52% of people reported being harassed online between 2022 and 2023, an increase from 40% in 2022. The new technological advancements make it easier than ever to spread negative, discriminatory, and pejorative comments to attack others with the click of a button. This is unfortunately an issue that affects many and often leads to violence, which is why it is important to identify and remove this online hate speech in order to minimize the spread. In this study, we explore the classification of online hate speech by comparing different vectorization and classification methods. We developed a set of eight classification techniques for identifying hate from a Twitter dataset containing hate speech and benign text examples. We cleaned and vectorized it in two different ways. Each of these sets is used to train deep learning, SVM, SGD, and random forest classifiers. All these methods achieved accuracy above 93%. The machine learning methods (SVM, SGD, and random forest) all achieved a nearperfect classification accuracy of 98.72%, with slight variations in precision and recall. The deep learning approach also showed improved performance with TF-IDF vectorization. Overall, the choice of vectorization had minimal impact on accuracy, and all models performed consistently well. © 2024 IEEE.

关键词： Adversarial machine learning

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A Real-Time Framework for Automatic Sarcasm Detection Using Proposed Tensor-DNN-50 Algorithm 4th

A Real-Time Framework for Automatic Sarcasm Detection Using ...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Siddesh, G.M. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819726103

Social media platforms such as Twitter, Facebook, and Instagram are vast repositories of trending global news. They generate an enormous amount of data, offering a valuable resource for both academic researchers and IT marketing teams. These data can be harnessed for various insights and applications. A prominent and pressing research area within this data is the detection of sarcasm and fake news. The internet is rife with misleading and false information, often causing widespread confusion. Current research primarily categorizes news as either real or fake, using machine learning algorithms. However, this binary approach struggles with news that is partially true yet contains elements that could be deemed fake. To address this issue, our work introduces a real-time framework for sarcasm detection, utilizing an innovative deep learning technique called Tensor-DNN-50 for multiclass classification. The TDNN-50 model not only classifies news articles based on authenticity but also quantifies the level of sarcasm within them. Comparative analysis against leading techniques like SVM, att-RNN, and EANN demonstrates the superiority of our proposed algorithm. It achieves an impressive accuracy of 96.4% for two-class classification, 94.32% for four-class classification, and an astonishing 99.1% for seven-class classification. These results underscore the potential of our Tensor-DNN-50 Model in effectively addressing the intricate challenges of sarcasm detection and news classification with unparalleled accuracy across multiple classes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Support vector machines

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