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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Nirmala Devi, K. Jayanthi, P. Rajasekar, Vani Rathinasamy, Rahul Dhandapani, Pradeep Rasil Risam, F. Dept. of Computer Science Kongu Engineering College Erode India

ISBN: (纸本)9798331527549

Social media's widespread use has made it easier for people to communicate openly, but it has also fuelled the growth of cyberbullying, which has seriously harmed victims' emotional and mental health. The intricacy of human language and the variety of abusive behaviours make it difficult to detect cyberbullying. To solve this problem, numerous studies have used deep learning (DL) and machine learning (ML) algorithms over time. However, deep learning (DL) algorithms have transformed text analysis by automatically extracting features and comprehending contextual dependencies in language, while machine learning (ML) techniques are limited in their ability to handle complex and contextual linguistic patterns. Building upon this framework, this study investigates the use of transformer-based models—more especially, BERT—for the detection of cyberbullying. Out of the proposed models, DistlBERT gives the best accuracy, precision, recall, and F1-score of 98.87%,98.87%, 98.87 and 98.86% respectively. © 2025 IEEE.

关键词： Adversarial machine learning

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Integrated Framework for Equitable Healthcare AI: Bias Mitigation, Community Participation, and Regulatory Governance 14

Integrated Framework for Equitable Healthcare AI: Bias Mitig...

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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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Enhanced Realtime Sign Language Detection and Multilingual Translation System

Enhanced Realtime Sign Language Detection and Multilingual T...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Kakkar, Madhur Daniel, John Sudha, D. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331538538

Sign language is the communication medium that is utilized by the hearing-impaired community. The medium is visual and it uses hand signs and symbols for communication. Over 300 different types of sign languages present in the world today. Sign language uses gestures and hand movements for the majority of the words and finger spellings for the rest. Nonverbal speakers face a lot of challenges while interacting with verbal speakers. Even non-verbal speakers who speak different sign languages face difficulties communicating with each other. This project proposes an enhanced real-time system which detects gestures and finger spellings of multiple sign languages, and translates them into multiple spoken languages. The proposed system aims to utilize CNN network for finger spelling recognition and Long Short-Term Memory (LSTM) network for gesture interpretation. The system will use computer vision for accurate gesture detection and Google Translator API for recognizing signs which will be translated into multiple spoken languages. Initial tests with isolated datasets indicate promising accuracy for both finger spelling and gesture recognition. The goal of the completed system is to help aid communication between verbal and non-verbal speakers and to provide an efficient mode of communication between the two communities. © 2025 IEEE.

关键词： Interactive computer systems

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SARCASAM Analysis in Social Networks Using Deep Learning Algorithm 4

SARCASAM Analysis in Social Networks Using Deep Learning Alg...

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

作者： Priya, M. Kumar, Vijaya K Vennila, P. Prasanna, M.A. Dept. of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam611002 India Dept. of Computer Science and Engineering Women's Engineering College Puducherry605008 India Dept. of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy621112 India

Twitter is the one of the biggest social media sites, where users may share their thoughts, ideas, and opinions as well as discuss current events and live tweets. In the subject of opinion mining, creating reliable and effective algorithms for sarcasm detection on Twitter is an intriguing task. Sarcasm is the use of positive language to convey depressing emotions while speaking in opposition to one's own intentions. Sarcasm is frequently employed on social networking and micro blogging platforms, where users can offend others and find it difficult to express their true feelings. The deep learning technique utilised in the current algorithms to identify these sarcastic tweets has the limitation of not being able to predict continuous variables. A novel deep learning algorithm is proposed to identify both positive and negative terms as well as sarcasm in comments. Deep neural networks are used to classify the comments into positive and negative word categories. Customers' opinions are mined using sentiment analysis to find and extract information from the text. Sarcastically stated statements from social networking sites can be quickly categorised and recognised by using VADER (Valence Aware Dictionary and Sentiment Reasoner). © 2025 The Authors.

关键词： Tweets

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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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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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Energy Efficient LEDCA Analysis for Improved Throughput in Wireless Sensor Networks 6

Energy Efficient LEDCA Analysis for Improved Throughput in W...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Padmageetha, B.G. Naik, Pramod Kumar Patil, Mallanagouda Dept.of Computer Applications Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering RVITM Bengaluru India

ISBN: (纸本)9798331522667

Wireless Sensor Networks (WSNs) face significant challenges in terms of energy conservation, scalability, and data throughput owing to their limited node energy resources. This paper presents a low-energy data-centric algorithm (LEDCA), a novel energy-aware clustering and routing protocol designed to optimize energy efficiency and improve throughput in WSNs. Through simulations, LEDCA demonstrated a Packet Delivery Ratio (PDR) of up to 90%, throughput of 255,217 bps, and average latency of 159 ms, validating its efficiency in resource-constrained environments. The algorithm integrates residual energy awareness by prioritizing high-energy nodes for Cluster Head (CH) roles, dynamically adjusting routing paths, and balancing the network load to prevent premature node failure. By applying an Energy Mapping Node and a real-time Energy Mapping Table, LEDCA minimizes re-clustering, reduces data redundancy, and ensures stable data transmission even under high-density networks. Simulation results also show LEDCA's scalability of LEDCA, with A9_T_200 achieving the highest throughput under extended transmission ranges and A3_T_200 achieving the lowest latency, making it suitable for energy-sensitive and latency-critical applications. Future enhancements, such as integrating Non-Orthogonal Multiple Access (NOMA) and dynamic resource allocation, could further improve LEDCA’s interference management and scalability of LEDCA for IoT and 5G/6G networks, addressing the needs of next-generation applications. © 2025 IEEE.

关键词： Resource allocation

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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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A Hybrid Approach Using Cryptography and K-Least Significant Bit Steganography Algorithm in Cloud Computing 1

A Hybrid Approach Using Cryptography and K-Least Significant...

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

作者： Goyal, Anshika Gupta, Bhumika VMSB Uttarakhand Technical University Dept. of Computer Science & Engineering Dehradun India G.B. Pant Institute of Engineering & Technology Dept. of Computer Science & Engineering Pauri India

ISBN: (纸本)9798331518578

The exponential growth of communication technologies has broadened the cloud computing ecosystem horizon to meet main communication needs. However, in-parallel upsurge in online attacks has alarmed industries to ensure optimal data security. The combination of steganography and cryptosystems has been recognized as an enhanced security model for data transfer among the main security systems now in use. In this paper, a hybrid approach using cryptography and K- Least Significant Bit Steganography algorithm in Cloud Computing (HYBC-KLSBS-CC) are proposed. Initially, input text data is retrieved from cloud storage and compressed using Huffman Compression Coding (HCC) to reduce data size. The compressed data is then encrypted with a Hybrid Cryptography Algorithm (Hyb-MRFC-ALC), combining Modified Random Fibonacci Cryptography and Asymmetric Lightweight Cryptography to ensure robust encryption. Finally, encrypted data is embedded within an image using the K-Least Significant Bit (KLSB) steganography technique, providing additional layer of security. The proposed method enhances data confidentiality, integrity, and storage efficiency in cloud environments. The proposed HYBC-KLSBS-CC approach attains 26.89%, 29.49% and 18.64% lower PSNR and 26.89%, 29.49% and 18.64% lower MSE when analyzed through existing techniques like data security dependent on cryptography steganography related with OTP procedure and Huffman coding in the cloud environment (CS-OTP-CE), a novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control (AES-OTP-RSA-CC) and Crypto-Stegno dependent model for securing medical information on IOMT platform (CS-SMI-IOMT). © 2025 IEEE.

关键词： Cloud platforms

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Exploring Taiwan Monkeypox Virus (MPOX/MPXV) Clade IIb by Establishing Dataset and Clustering by using Sequence and Image Similarity Comparisons 24

Exploring Taiwan Monkeypox Virus (MPOX/MPXV) Clade IIb by Es...

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7th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2024

作者： Wang, Tzung-Wei Li, Ming-Kuang Lou, Der-Chyuan Lin, Chun-Yuan Dept. of Electronics Engineering Chang Gung University Taoyuan Taiwan Dept. of Computer Science and Information Engineering Asia University Taiwan Taichung Taiwan Dept. of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan

ISBN: (纸本)9798400717437

Based on the MPXV Clade IIb cases in Taiwan, 175 segments were identified from OPG001 to OPG210. The first focus of this paper is on sequence analysis of the MPXV cases in Taiwan, detailing the procedures, including semi-automated segmentation and dataset creation from a large number of MPXV Clade IIb nucleotide sequences found in the NCBI and GISAID databases. The second feature highlights the pioneering establishment of an MPXV Clade IIb RNA 2nd structure image dataset in Taiwan, as such a dataset does not yet exist globally. The third characteristic involves the use of unsupervised learning methods for the rapid clustering of MPXV Clade IIb RNA 2nd structure images. This approach allows not only for the quick detection and clustering of unknown MPXV Clade IIb cases but also for the rapid identification of new MPXV mutations and structural changes. Compared to traditional sequence analysis, this new hybrid systematic method significantly enhances overall efficiency, improving processing speed by at least 24 hours when handling the alignment of 10 MPXV sequences from Taiwan. Future plans include using this prototype analytical model and method to accelerate the analysis of critical lethal factors in MPXV Clade IIb, such as D14L, ultimately helping biologists reduce the time required for analysis and antiviral drug development. © 2024 Copyright held by the owner/author(s).

关键词： Nucleotides

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