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ICT: Iterative Clustering with Training: Preliminary Results

ICT: Iterative Clustering with Training: Preliminary Results

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International Conference on New Trends in Computing sciences (ICTCS)

作者： Ahmad B. Hassanat Ahmad S. Tarawneh Ahmad S. Alhasanat Mansoor Alghamdi Khalid Almohammadi Zakaria Tarawneh Dept. Computer Science Faculty of Information Technology Mutah University Karak Jordan Dept. Data Science and Artificial Intelligence Mutah University Karak Jordan School of Foundation Studies and Mathematics Oryx Universal College Doha Qatar Dept of Mathematics Al-Hussein Bin Talal University Maan Jordan Dept. of Computer Science Applied College University of Tabuk Tabuk Saudi Arabia CiTIUS Universidade de Santiago de Compostela (USC) Spain

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

We introduce Iterative Clustering with Training (ICT), a hybrid approach that combines clustering with iterative classifier-based refinement. Unlike traditional clustering methods, ICT leverages supervised learning to reassign ambiguous boundary points, improving cluster quality. The proposed approach starts with an initial clustering phase, followed by the computation of silhouette scores to separate high-confidence core points from uncertain boundary points. A classifier (e.g., Random Forest) is then trained on core points to predict labels for boundary points, refining clusters in an iterative process until convergence is achieved. To avoid trivial clusters, minimum cluster ratio constraints are implemented. Experimental results on multiple benchmark datasets show that ICT outperforms traditional clustering methods such as k-means, HDBSCAN, and spectral clustering across several evaluation metrics, such as Adjusted Rand Index, Normalized Mutual Information, and Accuracy. ICT particularly excels at handling clusters that overlap, have noise, and complex geometries, where standard methods often fail. Key advantages include adaptive core selection, iterative refinement, and classifier flexibility, making ICT suitable for applications in customer segmentation, anomaly detection, and biological data analysis.

关键词： Training Measurement Clustering methods Roads Refining Supervised learning Semisupervised learning Iterative methods Standards Unsupervised learning

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YOLOv8-Based Pothole Detection for Autonomous Driving Safety

YOLOv8-Based Pothole Detection for Autonomous Driving Safety

引用

data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Poongothai P Sabareswari T C R Ramesh Veeramalai Sankaradass R. Ponnusamy Dept of Mathematics Chennai Institute of Technology Tamil Nadu India Dept of Electronics and Communication Engineering Chennai Institute of Technology Tamil Nadu India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Dept of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu India Dept. of Artificial Intelligence and Data Science Madha Engineering College Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Undeniably, autonomous vehicles are the most dramatic evolution of transportation yet. They offer new safety, efficiency, and convenience levels that have never been seen before. Still, some natural and unexpected conditions continually strike a blow against the safety of self-driving systems, such as potholes on roads. This paper introduces a new, cloud-based safety solution for autonomous vehicles through artificial intelligence and implementation with the YOLOv8 object detection model, coupled with the deep learning platform Weights & Biases, or wandb. As the real-time pothole-detecting system is developed, issues related to navigation appear while increasing the safety level. This work adopts the YOLOv8 object detection model in the use case due to its higher accuracy and much faster object detection rate. This can classify and predict the object boundaries of a single network, thus being a good fit for quick and accurate detection. Upgrades will be made in sync with new conditions in roads and types of potholes as the right solution gains ready to change the future of transportation about autonomous transportation, paving the path for safer and more reliable self-driving vehicles. It directly addresses the acute challenge of pothole-related hazards. It builds the way to an even safer and more efficient future for autonomous mobility, thus showing tremendous potential for transforming advanced AI technologies.

关键词： Accuracy Navigation Roads Transportation Object detection Real-time systems Safety Reliability Artificial intelligence Autonomous vehicles

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RadChain Connect: Integration Between Blockchain and FreeRADIUS for Secure Authentication in Wi-Fi Networks/Web Environment

RadChain Connect: Integration Between Blockchain and FreeRAD...

引用

International Conference on Information Networking

作者： Eduardo Freitas Hoffmann Caciano Machado Carla Merkle Westphall dept. of Computer Science and Statistics Federal University of Santa Catarina Santa Catarina Brazil Data Processing Center Federal University of Rio Grande do Sul Porto Alegre Brazil

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

User authentication in web environments and Wi-Fi networks is crucial due to the increase in fraud and invasions. Traditional methods, such as WPA2-PSK, have vulnerabilities, making the use of more secure solutions necessary. RadChain Connect is a proposal that combines blockchain with FreeRADIUS and self-sovereign identity, aiming to provide an efficient authentication system. Initially, it focuses on web authentication in universities, enhancing security at login. In the future, there are plans to expand the solution to Wi-Fi networks, leveraging the versatility of FreeRADIUS and the security of the EAP protocol.

关键词： Smart contracts Authentication Robustness Blockchains Fraud Security Proposals Internet of Things Wireless fidelity Business

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AutoMediVision:A Deep Learning System for Accurate Pharmaceutical pill identification

AutoMediVision:A Deep Learning System for Accurate Pharmaceu...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： G Rajeswarappa B. Venkata Sivaiah M. Navya Y. Lasya Shaik Mohammad Sameer V. Varun Kumar Department of CSE G Pulla Reddy Engineering College Kurnool Dept. of Data Science School of Computing Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupathi India Dept. of Computer Science and Systems Engineering(CSSE) Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The goal of this project is to build a high performance system that offers prediction of pill information from images, the pill name, medical use and recommended dosage by using MobileNet which is a lightweight and efficient deep learning model. This system serves as an important tool for healthcare professionals and patients as it offers a means of reducing risk because of the large number of medications out there. For this task, MobileNet was chosen for its high accuracy and good balance between computational efficiency and high accuracy, and hence can be deployed on mobile devices. Having trained the model on a wide variety of pills with class wise annotations, it generalized well for diverse pills. The output of the system is the pill name, primary medical indications and common dosage instructions that help in safer medication management by supporting healthcare providers in providing better care and patient safety.

关键词： Deep learning Accuracy Computational modeling Medical services Mobile handsets Safety Standards Research and development Biomedical imaging Pharmaceuticals

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Sentiment Analysis of Movie Reviews: Optimizing Predictions Through TF-IDF Vectorization

Sentiment Analysis of Movie Reviews: Optimizing Predictions ...

引用

Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Mohd Irfan Anne Sreyas Venkatesam Hariharan Shanmugasundaram Mahendra Kumar Gourisaria Subrata Chowdhury Sudhansu Shekhar Patra Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad Telangana India Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India School of Computer Engineering 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)9798331527549

ISBN: (纸本)9798331527556

Sentiment analysis model is aimed to accurately interpret movie reviews, addressing challenges like sarcasm and noisy data. By integrating multiple machine learning classifiers Naive Bayes, Random Forest, K-Nearest Neighbors, Logistic Regression, Decision Tree, and XGBoost—the model enhances sentiment prediction accuracy. The Term Frequency-Inverse Document Frequency (TF-IDF) method is used to extract features from a dataset of 50,000 movie reviews. Performance is evaluated using accuracy, precision, recall, and F1-score, with Logistic Regression achieving the best results, including an highest accuracy of 84.33 % and an F1-score of 0.847. In comparison to traditional methods, this multi-classifier approach offers a deeper understanding of movie reviews and its influence on audience decisions. The study demonstrates the potential of using these machine learning techniques to provide real-time, scalable sentiment analysis, which is crucial for filmmakers and marketers seeking insights into audience satisfaction and box office performance.

关键词： Sentiment analysis Logistic regression Accuracy Reviews Predictive models Motion pictures Feature extraction Real-time systems Noise measurement Random forests

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AI-Powered Personalized Deep Learning Framework for Early Detection of Glioblastoma Using Multi-Modal MRI data

AI-Powered Personalized Deep Learning Framework for Early De...

引用

data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Sunitha T Veeramalai Sankaradass V.K Manindra Manish Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India Bachelor of Medicine and Bachelor of Science Dhanalakshmi Srinivasan Institute of Medical Sciences and Hospital Perambalur India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The prognosis of glioblastoma, which stands as the most malignant variant of a brain tumour, is clearly unfavourable due to the late diagnosis and the severe heterogeneity it is known to possess. Standard diagnostic techniques nowadays that employ MRI data quite often have a low-level performance in terms of diagnosing glioblastoma during its early stages owing to the diverse manner in which the tumours may grow in various patients. In light of this background, we present a new deep learning framework suitable for multi-modal MRI data and aimed at improving glioblastoma prognosis at an early stage. The framework utilizes convolutional neural networks (CNNs) to obtain features from T1, T2, and FLAIR MRI images, and combines this with an AI-guided individualized diagnostic system. This model utilizes relevant clinical data such as age and genetic data in a more individualized manner to meet patient’s needs during diagnosis. One more novel feature of our approach is the ensemble learning that combines CNNs with RNNs to detect minor tumour deposits more effectively over time. The proposed framework delivered an AUC of 0.94 on large glioblastoma MRI databases, thus progressing the early-stage diagnosis of the tumour type which was the aim. This advanced personalized method not only allows for superior diagnosis but also improves on the treatment plans that are available for such patients. Further development will concern clinical verification as well as increasing the capacity of the framework.

关键词： Deep learning Accuracy Sensitivity Magnetic resonance imaging Genetics Feature extraction History Prognostics and health management Standards Tumors

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FogMedX-Transform: A Transformer-Based Task Interoperability Framework for Energy-Efficient Fog-Enabled IoMT

FogMedX-Transform: A Transformer-Based Task Interoperability...

引用

International Conference on Inventive Computation Technologies (ICICT)

作者： R Jayakarthik Sajja Suneel Jayendra Gopal Thatipudi V. Jaganraja Minesh Vohra Gopinath D Dept of Computer science Saveetha College of Liberal Arts and Sciences SIMATS Department of CSE(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Dept. of Computer Science University of North Texas Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India School of Mechanical Engineering Lovely Professional University Phagwara Department of Computer Science Kristu Jayanti College (Autonomous) Bengaluru Karnataka India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

The increasing speed of Internet of Medical Things (IoMT) development requires framework approaches that secure, efficient, and scalable for healthcare data management. The study presents FogMedX-Transform as a Transformer-based task interoperability framework purposebuilt for energy-efficient fog-enabled IoMT systems. A customized Transformer design utilizing multi-head selfattention allows the framework to deliver timely critical healthcare data processing alongside optimized energy usage. This model demonstrated a 98.7% task interoperability success rate coupled with a 97.5% anomaly detection accuracy that exceeded results from conventional models based in fog environments and the cloud. The framework improved performance through latency reduction between 50% and 70% and energy consumption minimization reached between 25% to 40% thus proving its effectiveness in resource administration. The scalability tests proved steady system functionality when dealing with different device quantities and built-in security features blocked data breaches and had a false positive rate of 2.3% during testing. FogMedX-Transform demonstrates the capability for healthcare transformation through its union of fog computing and deep learning technology integration according to test results.

关键词： Deep learning Performance evaluation Internet of Medical Things data breach Transformers data processing Energy efficiency Real-time systems Interoperability Anomaly detection

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Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： RVS Praveen Meenakshi Maindola Anandhi R J Nagarjuna Thandra Saloni Bansal Vishal Sharma Digital Engineering and Assurance LTIMindtree Limited Hyderabad Telangana India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Department of Information Science Engineering New Horizon College of Engineering Bangalore Dept. Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana Department of Computer Engineering and Applications GLA University Mathura Lovely Professional University Phagwara India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged.

关键词： Deep learning Autonomous underwater vehicles Accuracy Transfer learning Object detection data augmentation Real-time systems data models Environmental monitoring Convolutional neural networks

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Comparative Analysis of Machine Learning Techniques for Arabic Text Classification Using the SANAD dataset

Comparative Analysis of Machine Learning Techniques for Arab...

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Mechatronics and Robotics Engineering (ICMRE), International Conference on

作者： Hani Attar Yousef Sharrab Saja Smadi Safa Jbaily Faculty of Engineering Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Data Science and Artificial Intelligence Isra University Amman Jordan dept. Computer Science Yarmouk University Irbid Jordan

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Applying Machine Learning (ML) models for Arabic text classification is a growing research field that has recently raised a large number of research. Generally, classifying text involves selecting a category based on its content from a list of pre-established categories. data classification is among the most popular Natural Language Processing (NLP) activities. Moreover, automatic article classifications, such as the Single-label Arabic News Articles dataset (SANAD), are desperately needed to improve the retrieval of the requested data, which this paper investigates. The ML leverages large datasets to improve efficiency; indeed, by locating focal spots within the text, the learning mechanisms facilitate attention tools better suited for handling lengthy texts. The proposed work employs a dataset of 7,000 articles of separately classified Arab news items and then breaks them into seven categories of 1,000 news stories each: politics, education, sports, religion, culture, finance, and medicine. The investigated SANAD consists of three primary datasets from three news portals: Akhbarana, AlArabiva, and AlCtrl. Finally, to obtain trustable information, this study implements a variety of Conventional ML techniques, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression model to classify the investigated dataset. Our results show that the suggested solution outperformed the analytical search in the Arabic language in terms of accuracy, whereas the Logistic Regression model shows the best results among the seven utilized publicly available dataset classifications.

关键词： Support vector machines Logistic regression Adaptation models Accuracy Text categorization Artificial neural networks Nearest neighbor methods Natural language processing Robots Sports

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Interpretable Analysis of Oral Mucosal Lesions Using U-Net and Quantum Approaches

Interpretable Analysis of Oral Mucosal Lesions Using U-Net a...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Aparna M Anna Liza Sibi Prachi Kumari Golla Ram Senthil Kumar T. Somasundaram K. Vindhya Savithri Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Dept. of Artificial Intelligence and Data Science School of AI Amrita Vishwa Vidyapeetham Coimbatore India Department of Mathematics Amrita School of Physical Sciences Amrita Vishwa Vidyapeetham Coimbatore India Dept. of Oral & Maxillofacial Pathology and Microbiology Amrita School of Dentistry AIMS Ponekkara PO Kochi India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Oral mucosal lesions are abnormal changes found in the tissue of the mouth. They can be painful and may include abnormal cell growths, as well as rare tongue and hard- palate disorders. They may result from infections, inflamma- tory processes, trauma, autoimmune conditions, or precancerous changes. A recent study found a 59.6% prevalence of oral mucosal lesions among elderly individuals, highlighting a signif- icant association with factors such as age over 65, male gender, tobacco habits, and denture usage. Thus, lesions can significantly impact a patient's quality of life, necessitating accurate diagnosis and timely intervention. In this study, we use a combination of two advanced technologies-U-Net, a deep learning model, and quantum computing-to analyze medical images of oral health. We use a publicly available dataset containing labeled images classified as ‘Lesion’ and ‘Oral Cavity,’ focusing only on lesion detection. Our results show that while the U-Net model achieves 95.02% segmentation accuracy, the quantum approach using QCNN achieves 74.5% accuracy. Although the quantum model does not outperform U-Net in segmentation, it demonstrates potential for future research in quantum-enhanced medical imaging. To measure how well our method works, we use different evaluation techniques, including Dice Coefficient, Intersection over Union (IoU), accuracy, precision, recall, and F1-score. Our results indicate that while quantum computing has shown high accuracy in previous research for classification tasks, its application to segmentation-particularly in oral lesion detection-faces challenges such as lower accuracy, quantum noise, and hardware constraints. This research highlights the potential of integrating AI and quantum approaches for medical diagnosis while acknowledging the need for further improvements in quantum seamcntation models.

关键词： Deep learning Image segmentation Quantum computing Accuracy Computational modeling Hardware Computational efficiency Lesions Artificial intelligence Medical diagnostic imaging

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