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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Chandra, Joydeep Kaur, Ramanjot Sahay, Rashi Dept. of Computer Science and Engineering Chandigarh University Punjab Mohali India Dept. of Computer Science and Engineering MRIIRS Faridabad India

ISBN: (纸本)9798331531935

Artificial intelligence (AI) has revolutionized healthcare by enhancing diagnostic precision, personalizing treatments, and optimizing clinical workflows. However, algorithmic biases arising from imbalanced training data disproportionately affect marginalized populations, perpetuating health disparities. For instance, a widely-used predictive model underprioritized Black patients by equating healthcare costs with illness severity, restricting access to essential care. To address these challenges, this paper introduces an integrated framework combining adversarial debiasing, reinforcement learning (RL)-driven threshold optimization, synthetic data augmentation, and community engagement strategies. Empirical evaluations using the MIMIC-III dataset demonstrate near-perfect Area Under the ROC Curve (AUC = 0.994) alongside significant reductions in demographic parity (DP = 0.000) and error rate differences across subgroups. While the RL-optimized threshold ensures no missed diagnoses, it increases false positives, highlighting the trade-offs between fairness and precision. This work underscores the importance of synthesizing technical, regulatory, and community-driven approaches to promote equitable AI deployments. By embedding participatory design principles and aligning with emerging governance frameworks like the European Union’s AI Act, the proposed methodology offers a scalable pathway toward transparent, accountable, and high-impact healthcare AI solutions. © 2025 IEEE.

关键词： Patient treatment

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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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Border Detection for Fishermen Using IoT 4

Border Detection for Fishermen Using IoT

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Divya, B. Vijayalakshmi, K. Alivn Ancy, A. Priyadharshini, J. NaveenKrishna, B.B. Harshavardhan, R. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Chennai Institute of Technology Dept of Electronics and Communication Engineering India Dept of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508845

At present, the marine security issue includes the protection of fishers and address the rescue in case of illegal border infiltration. To detect the border for fisherman, this study conducted a method of enhancement in border detection essentially utilising the Automatic Identification System (AIS) with the assistance of the Internet of Things (IoT). In the middle sea, to provide boat detection with high precision, the proposed model incorporates Very High Frequency (VHF) communication inspired by X-band radar. The system is able to detect and monitor the boats at sea. To provide the middle sea with a high level of detection and monitoring of boats, system architecture included VHF (Very High Frequency) communication, which was inspired by the X band radar system. The range of AIS was increased using VHF communication. The coastal monitoring stations receive the position and status data from fishing boats with the help of IoT devices. The new AIS technology includes IoT-enabled devices installed on fishing boats. The IoT devices situated on boats send information to coastal monitoring station after sending data. The advanced technology increases the software capabilities of AIS, which will be helpful in the situation where the only AIS information was not enough. The advanced software capabilities improved the range of AIS and the definition capability of the vessels which were identified in the ocean near borders. The Automatic Identification System (AIS) that is being used in the present day has been upgraded and the Internet of Things (IoT) has been introduced. As a result, the system is now capable of much more comprehensive and real-time tracking and monitoring, as well as communication for marine vessels. This combination of technologies strengthens the safety, efficiency, and management of maritime operations by giving ships the Opportunity to connect and exchange data with different sensors, devices, and networks, and thus offering a more detailed and dynam

关键词： Fishing vessels

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Optimizing Crop Yield through A Data-Driven Approach with IoT Data Analytics and Machine Learning Algorithms 15

Optimizing Crop Yield through A Data-Driven Approach with Io...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Moeed, Syed Abdul Jinukala, Akshaya Kolipaka, Bhavana Kalvala, Aakanksha Kusuma, Vijay Kumar Kakatiya Institute of Technology and Science Dept of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350370249

Agriculture has long been the backbone of the Indian economy, defining the country's social and cultural milieu. Some of the most common difficulties that farmers face includes selecting suitable crops for their area and utilizing the necessary fertilizers, which can lead to a drop in production. To address these challenges, precision agriculture is employed. Precision agriculture is a smart and modern farming methodology that uses IoT-based research to provide information on soil qualities, types of soil, and crop production statistics. This assists farmers in choosing the optimum crops to plant and provides fertilizer recommendations based on specific location characteristics, also predicting the likelihood of plant diseases. In this project, we introduce a recommendation system by applying machine learning models such as Support Vector Machine (SVM), Logistic Regression, Decision Tree, Random Forest, and Naïve Bayes on a crop dataset, which is entirely IoT-based. The system recommends the best crop for the location's particular parameters with the highest accuracy. The system achieved an accuracy of 99% for crop recommendation and 92% for plant disease prediction. This research utilizes soil nutrients and pH data as inputs to create a website that predicts the most suitable crops for a particular soil type and suggests appropriate fertilizers. Additionally, the system predicts plant diseases and provides information about the diseases, including suggested cures. Thousands of farmers will be able to access the system through a Flask-based web interface. © 2024 IEEE.

关键词： Crop Recommendation Data Analytics (DA) Fertilizer Recommendation Internet of Things (IoT) Machine Learning (ML) Plant disease prediction Precision Agriculture ResNet

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An Effective Anti-Money Laundering System Using Block Chain Technology

An Effective Anti-Money Laundering System Using Block Chain ...

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2024 International Conference on Smart Systems for Electrical, Electronics, Communication and computer engineering, ICSSEEC 2024

作者： Divya, B. Thara, P. Kavitha, S. Alvin Ancy, A. Preethi Sri, R. Dhanalakshmi, D. Sri Sai Ram Institute of Technology Dept of Computer Science and Engineering India Dept of Computer Science and Engineering India India Chennai Institute of Technology Dept of Electronics and Communication Engineering India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378177

In the developed financial services industry, identity is defined using government-issued identification cards. Nevertheless, the primary obstacles in the consolidation and authentication of these data in connection with large-scale anti-money laundering (AML) requirements have significantly increased the challenges for individuals seeking to enter the financial system. This renders it exceedingly unprofitable and a greater risk for banking systems to engage with developing economies that might lack even the most primitive forms of identification. Furthermore, the process of establishing a banking account is packed with difficulty as a result of protracted processing times, substantial report obligations, and ambiguity surrounding the utilization of confidential customer data. The objective of cash laundering is to return illegal funds to their source;therefore, money launderers typically employ robust financial structures to transport funds. Additionally, the level of money laundering activity may become more geographically concentrated as the amount of money laundered increases. For many years, the financial quarter has been working to reevaluate the concept of identity. Blockchain is an emerging computing structure that enables the integration of disparate data points from authoritative source vendors into a unified digital ledger of validated, unalterable, transparent, and cryptographically protected data. Furthermore, secrecy can be ensured by letting the appropriate person or group control how their identity is shared. A blockchain is a distributed, virtual ledger that is unchangeable and keeps track of transactions in chronological order and close to real time. In order for later transactions to be added to the ledger the community members also known also nodes, must agree on them. This establishes an ongoing management mechanism that oversees issues related to manipulation, errors, and statistics. After dispatch Information contained within a block cannot be

关键词： Laundering

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