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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Kunkri, Rajdeep Ray, Sanchita Saha St. Thomas' College of Engineering & Technology Dept. of Information Technology Kolkata India

ISBN: (纸本)9798331522100

In this paper a novel approach for generating and optimizing regular expressions for genetic sequences is presented. The proposed technique introduces a systematic process to create initial regular expressions, compress and optimize them, and validate their accuracy for sequence matching. The method ensures a precise and reliable match with the given sequences while reducing complexity through an efficient optimization process. The proposed approach is versatile, being applicable to DNA, RNA, protein sequences, or any formal language processing. The method has been tested using a set of sequences with lengths ranging from 64 to 512 characters, with 50-100 pseudo-random generated sequence pairs and 5-10 % mutations for each case. Although the regular expression optimization process consumes slightly more time compared to conventional methods, it significantly reduces the overall matching time. Additionally, the optimized regular expressions show a reduction in memory usage. The results demonstrate that the proposed approach is highly effective for Motif Discovery and Representation, Pattern Matching Algorithms, Comparative Genomics and Phylogenetic and Gene Regulation, and Promoter Analysis, making it suitable for applications in bioinformatics and computational linguistics. © 2025 IEEE.

关键词： Gene expression regulation

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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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Zero-Trust Architecture for Smart City Healthcare Systems 2

Zero-Trust Architecture for Smart City Healthcare Systems

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Alsofyani, Shahd Ishaque, Mohammad Nofal, Mostafa Jedah International College Dept. of Computer Science and Information Technology Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

This paper introduces a tailored Zero-Trust Architecture (ZTA) for healthcare systems in smart cities, addressing the security challenges posed by interconnected infrastructures. The model enforces continuous access validation through micro-segmentation, identity and access management (IAM) with multi-factor authentication (MFA), and device trust verification, significantly reducing vulnerabilities. AI-powered real-time monitoring and anomaly detection enhance the system's ability to identify and mitigate advanced persistent threats (APTs) and insider risks. Secure communication protocols, end-to-end encryption, and audit logging ensure data protection, regulatory compliance, and forensic capabilities. The proposed ZTA provides a scalable, future-proof solution that strengthens patient data security and safeguards healthcare infrastructure against evolving cyber threats in smart cities. © 2025 IEEE.

关键词： Patient treatment

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Active Power Decoupling Method based on Dual Active Bridge Converter without additional components 14

Active Power Decoupling Method based on Dual Active Bridge C...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Takeuchi, Kosuke Ohno, Takashi Watanabe, Hiroki Nakata, Yuki Itoh, Jun-Ichi Nagaoka University of Technology Dept. of Electrical Electronics and Information Engineering Niigata Nagaoka Japan Nagaoka University of Technology Dept. of Science of Technology Innovation Niigata Nagaoka Japan

ISBN: (纸本)9798331516116

This paper proposed an active power decoupling method using a Dual Active Bridge (DAB) converter to minimize the energy buffer in single-phase AC-DC converter applications. Single-phase converters commonly require bulky electrolytic capacitors due to their power pulsation at twice the grid frequency. However, the electrolytic capacitor often decreases the power density and lifetime of the power converter. Active power decoupling is one of the solutions to this problem by using a firm or ceramic capacitor as the energy buffer. However, the additional circuit decreases the conversion efficiency and power *** paper proposes the active power decoupling method using a DAB converter without additional components. The Proposed method applies the feed-forward control to determine the phase shift angle for the power decoupling capability. In addition, the ZVS condition with the proposed method is considered to clarify the design of the small DC-link capacitor. The validity of the proposed method is demonstrated by the experimental result. As the experimental result, it was confirmed that the second harmonic component of the output voltage was reduced by 91.2%. © 2025 IEEE.

关键词： DC-DC converters

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Quantum Cryptography and Post-Quantum Security: Safeguarding Cryptographic Protocols Against Quantum Threats 2

Quantum Cryptography and Post-Quantum Security: Safeguarding...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Ishaq, Muhammad Al-Anesi, Bushra Jeddah International College Computer Science & Information Technology Dept. Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

As quantum computing progresses towards practical realization, the security of existing cryptographic protocols, such as RSA and Elliptic Curve Cryptography (ECC), faces unprecedented risks. Quantum algorithms like Shor's Algorithm threaten to undermine the security of these widely used encryption schemes, necessitating the development of quantum-resistant alternatives. This paper explores the vulnerabilities of current cryptographic protocols in the quantum era and investigates quantum-safe cryptographic techniques such as lattice-based, hash-based, and code-based cryptography. Additionally, we delve into Quantum Key Distribution (QKD), a method that leverages the principles of quantum mechanics to securely distribute cryptographic keys, ensuring secure communication even in the presence of quantum adversaries. The integration of QKD with post-quantum cryptographic algorithms is proposed as a robust approach to future-proofing cybersecurity. The paper concludes with a discussion of practical challenges in implementing quantum-safe systems and highlights future research directions to strengthen cryptographic resilience against quantum threats. © 2025 IEEE.

关键词： Qubits

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Brain Tumour Detection using MRI Images 1

Brain Tumour Detection using MRI Images

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Jayashree Mallick, Ahmed Faraz Das, Ashutosh Singh, Abhiraj Shivaprakash, T. Preethi Salian, K. Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering India Vijaya Vittala Institute of Technology Dept of Computer Science and Engineering Bangalore India Nmamit Dept of Information Science and Engineering Nitte Udupi India

ISBN: (纸本)9798331529246

An essential diagnostic technique for brain tumors, Magnetic Resonance Imaging (MRI) enables early detection and improved patient outcomes. However, manual interpretation of MRI scans can be time-consuming and subject to human error. Deep learning has revolutionized medical image analysis by providing automated and accurate classifications, significantly enhancing diagnostic processes. This study leverages the pre-trained Convolutional Neural Network (CNN) model Xception to classify MRI brain data, demonstrating its effectiveness in identifying brain tumors. The model was trained and validated using a diverse dataset, achieving a high test accuracy of 98.78% and training accuracy of 99.96%. These findings highlight the potential of the Xception model in supporting clinical decision-making and reducing diagnostic workloads. The study contributes to the field by showcasing Xception's implementation, dataset preparation, model design, training, and evaluation. Furthermore, it emphasizes the importance of using deep learning techniques for reliable and efficient brain tumor classification, setting the groundwork for future advancements in automated diagnostic systems. © 2025 IEEE.

关键词： Convolutional neural networks

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Investigating the Impact of Chunk Time on Video Streaming Performance in TCP-based Networks 29

Investigating the Impact of Chunk Time on Video Streaming Pe...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Chatri, Yeganeh Akbari, Behzad Goudarzi, Pejman Dept. of Computer and Electronic Engineering Tarbiat Modares University Tehran Iran Dept. of Information Technology ICT Research Institute Tehran Iran

ISBN: (纸本)9798331523114

The exponential increase in video traffic on the Internet has generated a heightened demand for efficient video streaming solutions. HTTP Adaptive Streaming (HAS) has emerged as a viable strategy, facilitating dynamic quality adaptation in response to fluctuating network conditions. In HAS, media content is segmented into short time intervals, with each segment encoded in multiple bitrate-resolution combinations known as representations. This framework enables users to choose the most appropriate representation according to their current network connectivity. To enhance the selection of bit rates and elevate the Quality of Experience (QoE), it is crucial to conduct a precise assessment of network conditions. This research investigated the impact of chunk time on video streaming performance in TCP-based networks. Analyzing the effects of chunk time on both QoE and throughput, our findings indicate that similar to the adaptive nature of video representations that depend on network conditions, chunk time must also be adaptive. It is essential that the chunk time is tailored to the specific network environment, necessitating an adaptive chunk-time strategy for each resolution to ensure optimal streaming performance. © 2025 IEEE.

关键词： Chunk Time HTTP adaptive streaming QoE

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Exploring emerging spreaders through GCN-based link prediction and a novel centrality method 17

Exploring emerging spreaders through GCN-based link predicti...

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

作者： Nandi, Suman Maji, Giridhar Dutta, Animesh National Institute of Technology Dept. of Computer Science and Engineering West Bengal Durgapur India Asansol Polytechnic Dept. of Electrical Engineering West Bengal India

ISBN: (纸本)9798331531195

Exploring influential spreaders and predicting missing links in complex networks is essential for understanding and effectively controlling network dynamics. This paper presents a Graph Convolutional Network (GCN)-based link prediction method to estimate the probability of future link formation. We incorporate node features that capture local and global topological connectivity structures and feed these into the GCN model, where convolutional layers aggregate neighboring information and transform node features. This approach enables the model to capture structural patterns by integrating local and global information from neighboring nodes. In the final layer, the GCN model computes a prediction score representing the likelihood of an edge's existence, using insights gained during training. Finally, considering the predicted links, we update the network structure and introduce a novel centrality method called Emerging Spreader Centrality (ESC) to identify emerging spreaders within this augmented network. We conduct two separate experiments to evaluate the performance of the GCN-based link prediction and the ESC method, comparing their effectiveness with various state-of-the-art methods. Results demonstrate that our approach not only effectively predicts future links but also identifies emerging spreaders in the augmented networks. © 2025 IEEE.

关键词： Prediction models

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Cross-Domain Adaptation Through Adversarial Teaching for Unmanned Ariel Vehicles in Smart Cities 22

Cross-Domain Adaptation Through Adversarial Teaching for Unm...

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22nd International Learning and technology Conference, L and T 2025

作者： Mazhar Ul Haq, Qazi Yao, Leethar Lin, Bo-Yu Yuan Ze University Dept. of IBPI and Computer Science and Engineering Zhongli Taiwan National Taipei University of Technology Dept. of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798331532215

Person re-identification has been an important issue of surveillance systems in smart cities. However, this requires huge datasets to supervise deep learning models for accurately identifying and tracking people in smart cities. In this paper, we propose adversarial learning in integration with a mutual mean teaching model known as adversarial teaching to take the issue of domain adaptation. Initially, they learn a source dataset in a supervised way. This model information is then transferred to another model through unsupervised learning and pseudo-labels are generated by generative adversarial learning. The multi-class invariance is handled by adversarial learning. The performance of the proposed model is evaluated on two different datasets source and target datasets. Multiple experiments on Duke and Market datasets indicate higher accuracy of F-1 score on both datasets compared to previous methods, demonstrating the superior performance of the proposed method. © 2025 IEEE.

关键词： Adversarial machine learning

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Geometrically Shaped Constellation in Short-Packet Visible Light Communications for IIoT Applications

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IEEE Internet of Things Journal 2025年第11期12卷 18220-18234页

作者： Guo, Jia-Ning Zhang, Jian Gong, Chen Li, Longguang Yang, Xu Zhou, Jing Chen, Ru-Han information engineering university Henan Province 450000 China The CAS Key Laboratory of Wireless-Optical Communications School of Information Science and Technology University of Science and Technology of China Hefei230027 China The SJTU Paris Elite Institute of Technology Shanghai Jiao Tong University Shanghai200240 China Sixty-Third Research Institute National University of Defense Technology Nanjing China Dept. Computer Science and Engineering Shaoxing University Shaoxing China

Due to the high data rates, license-free operations and inherent security, visible light communication (VLC) is highly valued in industrial Internet of Things (IIoT) applications. Short packet communication, which offers ultra-low latency and efficient resource utilization, is suitable for the low-latency and massive connectivity demand of the IIoT system. It adopts finite blocklength codewords for data transmissions. In this paper, we present a general framework of designing geometrically shaped constellations in short-packet VLC with peak and average intensity constraints for IIoT applications. By leveraging tools from large deviation theory, we first characterize the second-order asymptotics of the optimal constellation shaping region under aforementioned intensity constraints, which serves as a good performance metric for the best geometric shaping in finite blocklength. To further incorporate a sufficiently large coding gain and a nearly maximum shaping gain, we construct multidimensional constellations by the nested structure of Construction B lattices, where the constellation shaping is implemented by controlling the boundary of the embedded sublattice, i.e., a strategy called coarsely shaping and finely coding. Fast algorithms for constellation mapping and demodulation are presented as well. As an illustrative example, we present an energy-efficient 24-dimensional constellation design based on the Leech lattice, whose superiority over existing constellation designs is verified by numerical results. © 2014 IEEE.

关键词： Network security

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