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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Chittem, Mahesh Babu Kumar Pradhan, Ashok SRM University AP Dept of Computer Science and Engineering Amaravati India

ISBN: (纸本)9798331531195

Transfer learning is a set of techniques to apply skills or knowledge from a source task to a target task that is different but related, while Hybrid Quantum-Classical Transfer Learning (HQCTL) model extends the skills learned with quantum feature extraction specifically for edge computing which lacks resources. HQCTL combined with quantum-derived characteristics enhances accuracy, time, and real-time computation when it comes to classical aspects such as object detection or image analysis. In experimenting with images datasets such as COCO and PASCAL VOC the distribution of the framework generally presented higher accuracy and lower costs in terms of computation compared to either a purely classical or quantum approach. Of course, quantum enhanced feature extraction is still far from known and has greater potential for HQCTL as it helps to advance data representation which is optimal for the strict real-time processing in the IoT periphery. Possible research avenues include the development of different quantum representations of the problem, enhancements of the approach interconnectivity with various edge substrates, and the application of the framework to new machine learning tasks such as video analysis and time series prediction. Through presenting the HQCTL framework the potential of hybrid quantum-classical models to enhance edge AI applications while offering reliability, scalabilty and efficiency is demonstrated. © 2025 IEEE.

关键词： Transfer learning

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Deep Learning Based Detection of Anti-Reflective Obstacles in VLC Systems 6

Deep Learning Based Detection of Anti-Reflective Obstacles i...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Plissiti, Marina E. Papaioannou, Christoforos Sfikas, Yiorgos Papatheodorou, Georgios Poulis, Simon-Ilias Efthymiou, Aristides Tsiatouhas, Yiorgos University of Ioannina Computer Science and Engineering Dept Ioannina Greece

ISBN: (纸本)9798350344349

In Visible Light Communication (VLC) systems, the existence of non-reflective obstacles in the field of view of the receiver may cause deterioration or complete blockage of the optical signal which leads to an error-prone decoding process. We observe that image processing techniques, in combination with deep learning, provide effective ways to deal with this problem. In this work, we propose an efficient technique for the decoding of the signal in frames with partially occluded areas. First, a deep learning detector is used for the extraction of the obstacle, resulting in the determination of the regions of interest in each frame. Then, in a second step the decoding of the signal is employed, based on image processing techniques. The proposed method is evaluated in terms of the size of the obstacle and it is compared with other decoding schemes, providing very promising results. © 2024 IEEE.

关键词： Visible light communication

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Optimizing Job Search: A Multi-Model Approach with DRNN-BES, Collaborative Filtering, and LLM-NER Resume Analysis 3

Optimizing Job Search: A Multi-Model Approach with DRNN-BES,...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Jayakumar, Naveenkumar Patil, Netra Pokal, Roshankumar S. Dev, Rudra Pratap Nair, Sidharth Vellore Institute of Technology School of Computer Science and Engineering Vellore India College of Engineering Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350354348

This research introduces an innovative job recommendation platform designed to optimize the job search process through advanced technology integration. The platform features a sophisticated Resume Analyzer leveraging NLP and ML to meticulously extract candidate information. Coupled with a Recommendation System that employs mixture models such as DRNN-BES and collaborative filtering, it delivers personalized job suggestions tailored to individual profiles. Additionally, the system utilizes state-of-the-art web scraping technologies to continuously update its database with diverse and current job listings from various online sources, ensuring a wide range of opportunities are accessible. This integration of technologies not only refines job-matching precision but also significantly improves the efficiency of connecting job seekers with appropriate opportunities. Initial experimental results underscore the enhanced effectiveness and accuracy of our recommendation system, particularly when incorporating user behavioral data into the collaborative filtering algorithm. This paper demonstrates the transformative potential of integrating AI into job seeking, offering a scalable, unbiased, and time-efficient approach to modern employment challenges. © 2024 IEEE.

关键词： Collaborative filtering

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Automation of Data Analysis and Web-Scraping to Value Used Items 3

Automation of Data Analysis and Web-Scraping to Value Used I...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Shriya, Y.S. Babu, Aarav Chandrashekhar, Naren Verma, Siddhant Jagdish Shanthala, P.T. PES University Computer Science Engineering Dept Bangalore India

ISBN: (纸本)9798350372120

Second-hand products such as phones, laptops, car's, etc. make up a substantial part of the market. They aid a sustainable lifestyle by reducing the use of new resources and diminishing waste disposal. With the evolution of second-hand shopping, pricing has become a very tedious and cumbersome process. Traditionally, models are pre-trained on static datasets. With the trends in the market being sporadic, the outcome of such models is greatly affected. One approach to counter this would be to update the dataset periodically, which requires significant resource allocation. This approach is better suited when dealing with a dataset that contains a large range of products together but may prove inefficient when encountering a specific item. Through this study, a pipeline is established that integrates real-time web scraping with machine learning, which is achieved by using the Selenium tool, automating data preparation of the web scraped data, and implementing a machine learning model. The model considers essential item features to provide accurate predictions for specific items queried by a user. This paper also helps us perform a comparative study of various machine learning models that are considered ideal for regression analysis. When dealing with a dataset, limited in size, the need to pick a model that can handle the sparsity in data becomes critical. Tuning the Random Forest model to 30 trees showed the best results when compared to the other regression models. It predicted accurate prices on multiple iterations with varying inputs, whereas the other models did not account for the change in the features. © 2024 IEEE.

关键词： Costs

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Automated Skin Cancer Classification and Detection Using Convolutional Neural Networks and Dermoscopy Images 4

Automated Skin Cancer Classification and Detection Using Con...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sara, Umme Das, Utpol Kanti Sikder, Juel Chowdhury, Sudipta Roy Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Sadar Rangamati4500 Bangladesh

The uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have greatly improved image-based diagnosis. In this study, we included a Skin Lesion Cancer feature extractor Convolutional Neural Network (SLC-CNN) model, which is used for both classification with the SVM classifier and segmentation with XGBoost for skin cancer. In our proposed system, a test image of skin cancer is taken and pre-processed for both classification and segmentation purposes. After applying pre-processing, the test image features are extracted using the SLC-CNN feature extractor, which features are used in SVM to classify the types of skin cancer (Benign and Malignant), and based on the classification result, a trained XGBoost model is called to segment the cancer region. We have tested our system using the dermoscopy image collection from the International Skin Imaging Collaboration (ISIC) and built it in Google Colab to best use the GPU. Our suggested approach has gained a segmentation accuracy of 95.25% and a classification accuracy of 99.6%. © 2025 The Authors. Published by Elsevier B.V.

关键词： Lung cancer

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Generate Topics and Filter Novelties at Scale with Pre-Trained Language Models: A GPT-2 Method 1

Generate Topics and Filter Novelties at Scale with Pre-Train...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Jeevashri, B. Uma Priyadarsini, P.S. Saveetha University Saveetha Schoole of Engineering Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518578

The exponential rise of digital material calls for the development of more sophisticated approaches to the formation of topics that guarantee semantic richness, diversity, and scalability within the content. This research introduces an innovative pipeline employing the pre-trained Generative Pre-trained Transformer 2 (GPT-2) model to tackle issues related to topic coherence, redundancy, and the effective processing of extensive textual datasets to integrates text segmentation, novelty filtration, and semantic coherence enhancement. Text chunking facilitates the processing of extensive datasets by partitioning information into manageable chunks, whereas novelty filtering utilizes Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and cosine similarity measures to remove redundancy. Experiments on a news dataset illustrated the pipeline's efficacy, with filtered GPT-2 outputs attaining higher coherence scores (0.85) than raw GPT-2 (0.75) and conventional approaches such as Latent Dirichlet Allocation (LDA) (0.45). Chunking was essential for sustaining scalability, with an ideal size of 800 tokens achieving a compromise between computational efficiency and precision. The methodology substantially surpassed traditional methodologies in producing distinctive, contextually relevant themes, rendering it appropriate for applications including content analysis, document organization, and knowledge discovery. Future endeavors will concentrate on optimizing for domain-specific datasets, multilingual applications, and minimizing computational overhead to improve adaptability and efficiency. © 2025 IEEE.

关键词： Scalability

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Deep Learning and Signal Processing Integration in Automatic Speech Recognition Framework Using Hidden Markov Models 1

Deep Learning and Signal Processing Integration in Automatic...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Godvin Mani, S. Rajesh Kumar, T. Saveetha University Saveetha School of Engineering Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331518578

Automatic speech recognition is one of the technologies that change the game regarding the transcription of spoken language to text. A new methodology combining both feature extraction methods, including the Mel-Frequency Cepstral Coefficients (MFCC) and deep embedding, with acoustic modeling hybrid approach such as hidden Markov model (HMM) and DNN, shall be presented in the paper. The results exhibit excellent accuracy gains, up to 97.8% by the Transformer-based models in wide-ranging acoustic conditions. The obtained results underscore critical factors that come into play such as advanced preprocessing, feature extraction, and hybrid architectures to push ASR forward to more complicated noisy and noisy speech applications. © 2025 IEEE.

关键词： Hidden Markov models

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Revolutionizing Hearing Health: Mobile-based Audiometry Assessment Enhanced by Machine Learning Integration 8

Revolutionizing Hearing Health: Mobile-based Audiometry Asse...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Kanimozhi, P. Jebasanthiya, P. Kumar, T.Ananth Hussain, Mohamed Inamul Ananth, Christo Preethi, E. Ifet College of Engineering Dept of Computer Science Engineering Villupuram India Holy Cross Engineering College Dept of Computer Science Engineering Tamilnadu Thoothukudi India Samarkand State University Uzbekistan

ISBN: (纸本)9798350386578

The Mobile-Based Audiometry Test Assessment with Machine Learning Integration is a user-friendly application for convenient and accurate hearing health assessment. This innovative app incorporates advanced machine learning algorithms to enhance the precision of hearing tests, allowing for early detection of hearing impairments. Users can effortlessly create profiles, securely store their test results, and access their hearing history. The application ensures the calibration of user hardware for accurate tests, closely mirroring clinical audiometry standards with a wide range of frequencies and decibel levels. Machine learning, particularly decision tree classification, is seamlessly integrated to improve the accuracy of audiogram analysis and classification, enabling the identification of varying degrees of hearing loss. The user-centric design and educational resources encourage awareness and proactive management of hearing health. This Mobile-Based Audiometry Test Assessment with Machine Learning Integration offers an accessible, technologically advanced solution for individuals to take charge of their auditory health and make informed decisions about their well-being. © 2024 IEEE.

关键词： Audiometers

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ENHANCING E-HEALTH CYBERSECURITY AND RESILIENCE: SHIFTING FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss deployment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

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TurfIt: An Instant Turf Booking Application

TurfIt: An Instant Turf Booking Application

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Rajeswari, A.M. Tamilselvan, V. Abishek, M.R.P. Dept. of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331512965

As sports grow in popularity need for effective online turf booking applications has also increased. Although some existing applications are faced with numerous challenges such as;restrictions on access, ineffectiveness in scheduling, and over-bookings, leading to frustrations and operational problems. This paper focuses on those problems by the presentation of an online turf booking application to streamline the booking process. The solution offers real-time updates, automatic payment, and safe transactions based on QR code. The more user-friendly the application is, the more efficient it will be. Firebase would be used for real-time data synchronization and the responsiveness of React interface ensures the smooth and conflict-free bookings. This solution would lead to increasing customer satisfaction as well as increasing the booking rates and effective utilization of sports facilities. © 2025 IEEE.

关键词： Customer satisfaction

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