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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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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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Real-Time IoT Cybersecurity using Machine Learning-based AI Threat Detection System to Train Generative Robots

Real-Time IoT Cybersecurity using Machine Learning-based AI ...

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Trends in Material science and Inventive Materials (ICTMIM), International Conference on

作者： P Srilakshmi Koushik Reddy Chaganti Talachendri Suryam Sweety Julia I D. Chaithanya Jami Kavitha Department of CSE G Pulla Reddy Engineering College Dept. of Computer and Information Systems University of the Cumberlands Department of CSE (DATA SCIENCE) Vignan Institute of Technology and Science Department of AI&ML Malla Reddy University Department of CSE CVR College of Engineering Department of CSE Sanketika Vidya Parishad Engineering College

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do not properly handle current cyber threats. The main goal of this study involves developing enhanced IoT cybersecurity through the development of a threat detection system which brings together adversarial training and deep learning models (CNN-LSTM) and Federated Learning (FL). The system enables distributed Internet of Things devices to work on security model development through Federated Learning while maintaining total privacy of their information. Security procedures controlled by generative artificial intelligence robots alongside real-time attack protection functions decrease security response durations. Through its Federated CNN-LSTM model the system upholds a 1.2% false positive rate alongside a 98.3% accuracy evaluation and 160 milliseconds of exact threat tracking time. The designed system sustains a minimal occurrence of incorrect alarm activations. The developed system provides real-time security for the Internet of Things framework because it enables adaptive protection systems while preserving user privacy in current IoT settings.

关键词： Training Privacy Protocols Federated learning Real-time systems Threat assessment Internet of Things Computer security Protection Robots

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Optimizing Production and Inventory Management for Defective Items in Manufacturing Systems

Optimizing Production and Inventory Management for Defective...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Khandakar Rabbi Ahmed Arafat Hossain MD Amaddus Bepary Asif Mahathir Mohammad Mustafizur Rahaman MD Arafat Dewan Miyan Research Institute International University of Business Agriculture and Technology Dhaka Bangladesh Dept. of Business Data Analytics Central Michigan University Mt Pleasant USA Dept. of Business Administration and Management Southern California State University Los Angeles CA Dept. of Business Administration and Management Southern California State University USA Dept. of Cloud Engineering Westcliff University Irvine CA Dept. of Computer Science Los Angeles City College Los Angeles USA

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Manufacturing defective items is one of the challenges for production efficiency and inventory control. Conventional approaches like Economic Order Quantity (EOQ) and Just-in-Time (JIT) often fall short of the flexibility needed to respond to quality variations in real time. It promotes defect detection and control by employing machine learning (ML) for inventory management. Four classifiers were tested using the UCI SECOM dataset (Bagging, AdaBoost, Gradient Boosting, and Random Forest) to identify defective items. We used effective preprocessing techniques to boost model performance, such as SMOTE for class balancing and PCA for reducing dimensionality and standardization. The bagging and random forest classifiers produced 96.6% and 96.9% test accuracies, with good precision and recall scores. Within this context, we propose an Adaptive Inventory Management Framework comprising machine learning algorithms incorporating predictive analytics to reduce waste, optimize production scheduling(s), and improve resource allocation. These results are consistent with defining Industry 4.0 strategies and show that ML has the potential to transform defect management and inventory optimization in manufacturing systems.

关键词： Production Quality control Inventory management Real-time systems Fourth Industrial Revolution Random forests Optimization Bagging Manufacturing systems Defect detection

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Hybrid K-Means and Firefly Algorithm-Based Load Balancer for Dynamic Task Scheduling in Fog Computing for Postoperative Healthcare Systems

Hybrid K-Means and Firefly Algorithm-Based Load Balancer for...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： N. Jagadish Kumar Rakesh Premkumar L. Maria Michael Visuwasam Gopalakrishnan Arjunan G. Yuyaraj C. Tharun Kumar Dept of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India School of Informatics and Computing Indiana University Bloomington USA Dept. of AI & DS RMK College of Engineering and Technology Puduvoyal Chennai India Accenture Bangalore India Dept of CSE (5 years Integrated) Sri Sai Ram Engineering College Chennai Tamil Nadu India Dept. of CSBS Sri Sai Ram Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

In fog computing for postoperative healthcare systems, efficient task scheduling and load balancing are crucial for improving response time, energy efficiency, makespan, and the overall performance of the system. This manuscript introduces a novel hybrid load balancing framework that amalgamates KMeans clustering with the Firefly Algorithm (FA) to tackle the dynamic task scheduling complexities inherent in fog-based healthcare environments. The K-Means methodology is employed to categorize tasks according to their computational necessities and resource requirements, thereby establishing clusters that facilitate optimized resource distribution. The Firefly Algorithm, recognized for its proficiency in addressing optimization challenges, is harnessed to ascertain the optimal scheduling arrangement within each task cluster by adept.y modifying task allocation among fog nodes in response to light intensity, which is directly proportional to the overall performance of the system. Empirical findings indicate that the proposed mechanism markedly surpasses conventional algorithms regarding the optimization of response time, makespan, cost, and energy consumption, thereby presenting a viable solution for the management of postoperative care within healthcare systems employing fog computing infrastructure. The results substantiate that our proposed model enhances response time by 18%, makespan by 17%, energy consumption by 15%, cost by 17%, and level of load balance by 18%.

关键词： Processor scheduling Heuristic algorithms Clustering algorithms Medical services Optimal scheduling Dynamic scheduling Load management Time factors Resource management Edge computing

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Adaptive Optimization and Resource Allocation (AORA) Model for IoT-Edge Computing Using Hybrid Newton-Raphson and Dolphin Echolocation Algorithm (HNR-DEA) Technique

Adaptive Optimization and Resource Allocation (AORA) Model f...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： N. Jagadish Kumar Rakesh Premkumar L. Maria Michael Visuwasam Gopalakrishnan Arjunan A. Shiny K. Dharani Dept of Data Science and Business Systems SRM Institute of Science and Technology Chennai Tamil Nadu India School of Informatics and Computing Indiana University Bloomington USA Dept. of AI & DS RMK College of Engineering and Technology Puduvoyal Chennai India Accenture Bangalore India Dept of CSE (5 years Integrated) Sri Sai Ram Engineering College Chennai Tamil Nadu India Dept. of CSBS Sri Sai Ram Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The swift expansion of Internet of Things (IoT) applications demands the implementation of real-time data processing and low-latency communication, thereby posing challenges to conventional cloud computing paradigms. This study confronts these issues by amalgamating edge computing with hybrid optimization methodologies, and introduces the AORA (Adaptive Optimization and Resource Allocation) framework. The AORA framework capitalizes on intelligent task scheduling, dynamic resource allocation, and energy-efficient approaches to augment the performance of edge *** research methodology encompasses the collection and preprocessing of data derived from IoT devices, feature extraction, prediction of resource demand, and the optimization of task offloading. It employs the Hybrid Newton Raphson and Dolphin Echolocation Algorithm (HNR-DEA) methodology, which synergizes Newton Raphson optimization technique for faster convergence rate around local optima with bio-inspired Dolphin Echolocation Algorithm (DEA) for comprehensive global *** Experimental outcomes reveal considerable advancements, including a 45% reduction in average latency, a 38% decrease in overall energy consumption, and a 23% enhancement in resource utilization. Furthermore, the model attained a 97% task completion rate alongside consistently elevated network throughput. Simulations were executed in CloudSim with 100 IoT devices generating dynamic workloads and edge servers equipped with multi-core processors. The experimental framework utilized Google Cluster data to replicate workload variations, thereby ensuring scalability and robustness. These results underscore the AORA model's potential as a resilient and scalable solution for real-time IoT applications.

关键词： Adaptation models computational modeling Scalability Prediction algorithms Real-time systems Internet of Things Resource management Newton method Optimization Dolphins

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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...

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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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CAAT-EHR: Cross-Attentional Autoregressive Transformer for Multimodal Electronic Health Record Embeddings

arXiv

引用

arXiv 2025年

作者： Olaimat, Mohammad Al Bozdag, Serdar Dept. of Computer Science and Engineering University of North Texas DentonTX United States Dept. of Mathematics University of North Texas DentonTX United States BioDiscovery Institute University of North Texas DentonTX United States Center for Computational Life Sciences University of North Texas DentonTX United States

Motivation: Electronic health records (EHRs) provide a comprehensive source of longitudinal patient data, encompassing structured modalities such as laboratory results, imaging data, and vital signs, and unstructured clinical notes. These datasets, after necessary preprocessing to clean and format the data for analysis, often remain in their raw EHR form, representing numerical or categorical values without further transformation into task-agnostic embeddings. While such raw EHR data enables predictive modeling, its reliance on manual feature engineering or downstream task-specific optimization limits its utility for general-purpose applications. Deep learning (DL) techniques, such as recurrent neural networks (RNNs) and Transformers, have facilitated predictive tasks like disease progression and diagnosis prediction. However, these methods often struggle to fully exploit the temporal and multimodal dependencies inherent in EHR data due to their reliance on pre-processed but untransformed raw EHR inputs. In this study, we introduce CAAT-EHR, a novel architecture designed to bridge this gap by generating robust, task-agnostic longitudinal embeddings from raw EHR data. CAAT-EHR leverages self- and cross-attention mechanisms in its encoder to integrate temporal and contextual relationships across multiple modalities, transforming the data into enriched embeddings that capture complex dependencies. An autoregressive decoder complements the encoder by predicting future time points data during pre-training, ensuring that the resulting embeddings maintain temporal consistency and alignment. CAAT-EHR eliminates the need for manual feature engineering and enables seamless transferability across diverse downstream tasks. Results: Extensive evaluations on benchmark multimodal EHR datasets, including MIMIC-III and ADNI, demonstrate the superiority of CAAT-EHR-generated embeddings over pre-processed raw EHR data and other baseline approaches. The proposed model excels in tasks s

关键词： Electronic health record

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD)

Blockchain Integration for Robust Intellectual Property Prot...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： P. Chinnasamy Subashini. B Nijanthan Nijanthan Madasamy Raja. G Devulapally Swetha Sreenivasulu Reddy L Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Data Science and Business Systems School of Computing SRMIST India Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu India Information Technology Paavai Engineering College Namakkal Tamilnadu India Dept. of Computer Science and Engineering Vaagdevi Engineering College Telangana India Department of Mathematics School of Advanced Sciences Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms.

关键词： Patents Technological innovation Costs Smart contracts Intellectual property Product development Blockchains Security Protection Resilience

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UDC-VIT: A REAL-WORLD VIDEO dataSET FOR UNDER-DISPLAY CAMERAS

arXiv

引用

arXiv 2025年

作者： Ahn, Kyusu Kim, JiSoo Lee, Sangik Lee, HyunGyu Ko, Byeonghyun Park, Chanwoo Lee, Jaejin Dept. of Data Science Seoul National University Seoul Korea Republic of Dept. of Computer Science and Engineering Seoul National University Seoul Korea Republic of Research Center Samsung Display Co. Ltd. Yongin Korea Republic of Mobile Display Electronics Development Team Samsung Display Co. Ltd. Yongin Korea Republic of

Under Display Camera (UDC) is an advanced imaging system that places a digital camera lens underneath a display panel, effectively concealing the camera. However, the display panel significantly degrades captured images or videos, introducing low transmittance, blur, noise, and flare issues. Tackling such issues is challenging because of the complex degradation of UDCs, including diverse flare patterns. Despite extensive research on UDC images and their restoration models, studies on videos have yet to be significantly explored. While two UDC video datasets exist, they primarily focus on unrealistic or synthetic UDC degradation rather than real-world UDC degradation. In this paper, we propose a real-world UDC video dataset called UDC-VIT. Unlike existing datasets, only UDC-VIT exclusively includes human motions that target facial recognition. We propose a video-capturing system to simultaneously acquire non-degraded and UDC-degraded videos of the same scene. Then, we align a pair of captured videos frame by frame, using discrete Fourier transform (DFT). We compare UDC-VIT with six representative UDC still image datasets and two existing UDC video datasets. Using six deep-learning models, we compare UDC-VIT and an existing synthetic UDC video dataset. The results indicate the ineffectiveness of models trained on earlier synthetic UDC video datasets, as they do not reflect the actual characteristics of UDC-degraded videos. We also demonstrate the importance of effective UDC restoration by evaluating face recognition accuracy concerning PSNR, SSIM, and LPIPS scores. UDC-VIT enables further exploration in the UDC video restoration and offers better insights into the challenge. UDC-VIT is available at our project site. © 2025, CC BY-NC-SA.

关键词： Restoration

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