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16th International Conference on Contemporary Computing, IC3 2024

作者： Keerthan Kumar, T.G. Adhith, Adhith Jayadeep, Nitin Gokulraj, M. Koolagudi, Shashidhar G. Department of Information Science and Engineering Siddaganga Institute of Technology Tumkur India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Surathkal India

ISBN: (纸本)9798400709722

In the realm of Natural Language Processing, the quest to simplify text is paramount, aiming to enhance accessibility and comprehension for all individuals. This study delves into the realm of English text simplification through a distinctive approach leveraging syntactic parse trees, known as syntactic simplification. Our methodology tackles two key aspects: the first involves streamlining compound sentences linked by coordinating conjunctions, while the second focuses on simplifying those connected by subordinating conjunctions, namely complex sentences. With our innovative approach, we achieved a notable BLEU score of 81.9, surpassing previous endeavors in the field. In an era marked by information saturation and diverse literacy levels, the need for clear and intelligible text is undeniable. This work not only sheds light on effective text simplification but also underscores its vital role in fostering universal understanding and communication. © 2024 Copyright held by the owner/author(s).

关键词： Syntactics

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SNR-Responsive Communication: Turbo Codes and BCH in a Dynamic HARQ Scheme for Enhanced Efficiency

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SN computer science 2025年第5期6卷 1-13页

作者： Chintha, Gnana Jyothi Prathapa, Chandana Sai Sri Hasitha Namburi, Yaswanth Chandavarkar, B.R. Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Karnataka Mangaluru 575025 India

Wireless communication systems face many challenges due to fluctuating channel conditions resulting in variable error rates and demands robust error management tactics. Traditional Automatic Repeat Request (ARQ) methods suffer from high communication overhead and latency and are inefficient in handling retransmissions. Although traditional Hybrid Automatic Repeat Request (HARQ) is effective, its widespread use is limited by high implementation costs. This underscores the need for adaptive systems that dynamically respond to changing channel conditions based on Signal-to-Noise ratio (SNR). This paper proposes the Dynamic HARQ (D-HARQ), which switches between ARQ, HARQ with Bose–Chaudhuri–Hocquenghem codes, and HARQ with turbo codes based on channel conditions during the transmission. Further, this paper incorporates a selective soft-combining technique by estimating the receiver end’s SNR. The main goal is to achieve the maximum possible throughput even at very low SNR values while maintaining optimal complexity. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adaptive HARQ BCH codes Error correction Selective soft combining SNR estimation Throughput optimization Turbo codes

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An Adaptation and Evaluation for Object Detection of Small Objects Using YOLO8

An Adaptation and Evaluation for Object Detection of Small O...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Chitra, M. Balasubramanian, Haripriya Srinivaas, V Lakshan Jayanth, Korada SRM Institute of Science and Technology Computer Science Engineering Department Chennai600089 India

ISBN: (纸本)9798331508432

Detecting small objects in aerial imagery, particularly from UAVs, presents unique challenges due to the reduced size of targets, complex backgrounds, and scale variations. Despite advancements in deep learning and improvements in object detection models, capturing small objects in UAV-captured images remains difficult. Conditions such as object occlusion, varying resolutions, and densely populated environments continue to impact the precision and recall of small object detection, requiring further architectural enhancements to improve performance in these *** this research, we introduce an enhanced object detection model for small-scale objects in UAV-captured imagery, addressing the unique challenges posed by varying object scales and densely packed environments. Our model incorporates several state-of-the-art improvements to the YOLOv8 architecture. First, we introduce the Multi-Scale Dynamic Attention Mechanism (MSDAM) to optimize feature extraction across multiple scales, enhancing the detection of small objects. Next, we employ a dynamic non-monotonic loss function named Weighted-Intersection over Union (WIoU) to refine bounding box regression, improving localization accuracy, particularly for small, hard-to-detect targets. To further boost performance, Squeeze-and-Excitation (SE) Layers are integrated, adaptively recalibrating feature maps to emphasize the most informative channels, thus enhancing feature representation. Additionally, Soft Non-Maximum Suppression (Soft-NMS) is applied in post-processing, reducing missed detections by preserving overlapping objects in densely populated scenes. Our experiments show significant improvements in precision, recall, and mAP, demonstrating the efficiency of the proposed methodology in accomplishing accurate and computationally efficient object detection for UAV applications. © 2024 IEEE.

关键词： Aerial photography

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Cyber-Physical Systems and IoT Intrusion Detection: A Hybrid Deep Learning Perspective 3

Cyber-Physical Systems and IoT Intrusion Detection: A Hybrid...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Prakash, S. Jaya Devi, D. Muthukumaran, N. Romila, R. Idhaya Engineering College for Women Department of Computer Science and Engineering Tamilnadu Chinnasalem India Maha Barathi Engineering College Department of Computer Science and Engineering Tamilnadu Chinnasalem India A.R. Engineering College Department of Computer Science and Engineering Tamilnadu Villupuram India Sri Muthukumaran Institute of Technology Department of Information Technology Tamilnadu Chennai India

ISBN: (纸本)9798331509675

Several vital resources are increasingly being protected by cyber-physical systems (CPSs), makes the detection of incidents on these systems critical. CPSs along with other domains, such as the Internet of Things (IoT), frequently use machine learning (ML) and deep learning (DL) approaches to combat intrusion detection. Existing approaches, however, frequently favor the deployment of advanced detection models over their usefulness in actual operations. This research work develops a hybrid intrusion detection technique by combining Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM). This work analyzes the experimental results to LSTM and CNN to develop an effective comparative approach. The experiments are conducted on the two datasets. The efficiency indicators examined are intrusion detection results such as accuracy, precision, recall, and f1score. This comparison indicates that the proposed hybrid algorithm is the preferable approach. © 2025 IEEE.

关键词： Network intrusion

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Stairdepth: a novel staircase detection through depth maps generated by depth anything V2

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International Journal of Information technology (Singapore) 2025年第4期17卷 2075-2086页

作者： Shekar, Avire Laxmi Chandra Chandrika, Mukkolla Bhuvana Naidu, Vakkalagadda Hemanth Muppalaneni, Naresh Babu Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing Kurnool Kurnool India

Detection of a staircase is an important task in the fields of both assistive technology and autonomous navigation with an aim to enable substantial improvement in safety and accessibility for both those with limited vision and those with mobility problems. Classic methods often perform poorly under challenging conditions, such as low lighting, irregular architectural arrangements, or occluded views. Whereas the integration of physical depth sensors has increased detection accuracy, this integration introduces increased complexity, cost and greater maintenance issues. Our work proposes a novel architecture capable of directly estimating depth maps from color images using a pre-trained model without relying on physical sensors for depth estimation. It detects the staircase efficiently, including the classification of stair lines into concave or convex, with the classification of surfaces as a tread or riser, hence localizing staircases accurately. Our proposed methodology demonstrated exceptional results, with 83.2% Precision, 90.8% Recall, and 72.2% Intersection over Union on stair line classification and 97.1% Pixel Accuracy and 82.9% Mean Pixel Accuracy on stair surfaces by outperforming the efficacy of current approaches pertaining to stair line and surface classification. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Depth image Segmentation Stair surfaces Staircase detection StairDepth Stairline

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A New Median Filter Circuit Design Based on Atomic Silicon Quantum-Dot for Digital Image Processing and IoT Applications

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IEEE Internet of Things Journal 2025年第13期12卷 23000-23007页

作者： Ahmadpour, Seyed-Sajad Avval, Danial Bakhshayeshi Navimipour, Nima Jafari Rasmi, Hadi Heidari, Arash Kassa, Sankit Misra, Neeraj Kumar Navin, Ahmad Habibizad Mosleh, Mohammad Hosseinzadeh, Mehdi Patidar, Mukesh Kadir Has University Department of Computer Engineering Istanbul Turkey Sakarya University Department of Information Systems Engineering Sakarya Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Yunlin64002 Taiwan Western Caspian University Research Center of High Technologies and Innovative Engineering Baku Azerbaijan Islamic Azad University Department of Computer Engineering Dez.C. Dezful Iran İstanbul Atlas University Faculty of Engineering and Natural Science Department of Computer Engineering Istanbul Turkey Symbiosis International Deemed University Symbiois Institute of Technology Pune India VIT-AP University School of Electronics Engineering Andhra Pradesh Amaravathi522237 India Islamic Azad University Department of Computer Engineering Tabriz Branch Tabriz*** Iran Islamic Azad University Department of Computer Engineering Dezful Branch Dezful Iran Duy Tan University Institute of Research and Development Da Nang Viet Nam Indore Institute of Science and Technology Department of Electronics and Communication Engineering Madhya Pradesh Indore453111 India

Digital Image Processing (DIP) is the ability to manipulate digital photographs via algorithms for pattern detection, segmentation, enhancement, and noise reduction. In addition, the Internet of Things (IoT) acts as the eye and system for all DIP in various applications. It can possess a camera or another image sensor in order to capture real-time data from its environment. All vital data is processed by image processing in such a way that it recognizes the object, detects an anomaly, and automatically decides in real-time. In addition, in an IoT system, the median filter is the technique used for noise reduction by substituting the value of the pixel with the central value of the surrounding pixels. It provides speed and efficiency for quick analysis in all IoT systems. However, the images can get corrupted, especially in resource-constrained IoT devices with small cameras, because of random glitches. Moreover, using new quantum technology like atomic-scale silicon dangling bond (DB) logic circuits, which have advanced in fabrication and become a strong contender for field-coupled nano-computing, can solve previous problems in IoT systems. In this paper, we propose a unique quantum CSM based on two new proposed Mux and De-mux. The proposed CSM can be used for computational circuits like median filter circuits (MFC) in a wide range of digital circuits, specifically IoT devices. The proposed design is verified and validated using the powerful SiQAD tool. When comparing CSM to the newest designs, the suggested quantum circuit uses 85% less energy and takes up 61% less area. © 2014 IEEE.

关键词： Dangling bonds

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Deep Learning-Based Diagnosis System for Liver Cancer Through Multimodal Determination for Early Treatment

Deep Learning-Based Diagnosis System for Liver Cancer Throug...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Fatima, N. Sabiyath Singh, Ramendra Swathi, Akuthota Choudhary, Amar Ahmed, Azhar Sharma, Chhaya B.S. Abdur Rahman Crescent Institute of Science & Technology Department of Computer Science and Engineering Chennai India Ghaziabad India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalli Rangareddy India Alliance University Department Electronics and Communication Engineering Bengaluru India Kcc Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350379716

The present research provides an analysis of the development and implementation of a deep learning-based diagnosis system for liver cancer. The provided system was developed using a dataset encompassing 2300 sensor readings, including imaging, clinical, and genomic data. A range of deep learning features was implemented to improve the early detection of liver cancer such as CNN, VGG 16, VGG 19, and Inception V3. The data were divided into two categories: one is for training, and another used for testing. The results of the data analysis have shown that VGG 19 provides the highest diagnostic accuracy of 98.76%, with VGG 16, Inception V3 and CNN displaying the levels of 94.5%, 93.4%, and 90.25%, respectively. The Model performance analysis was also augmented at the application of precision, recall, and F1 score indicators, as well as using AUC-ROC values which highlighted VGG 19 as the most effective algorithm. Another method of evaluation was the application of confusion matrices for plotting the results based on the number of true positive, true negative, false positive, and false negative detections, where all the samples from the dataset were added to each model calculation. The obtained results have shown the importance of multimodal data for the increase of diagnosis accuracy and revealed the promising potential of advanced computation techniques in medical analytics. In such a way, it can be stated that the developed deep learning models contribute to the ability of healthcare specialists in the definition of liver cancer at early stages, which allows timely interventions and facilitates the development of a range of personalized treatment protocols. The application of such algorithms has the potential to reshape liver cancer diagnostics, where major tasks are associated with further improvement and validation of the developed algorithms based on clinical case studies. © 2024 IEEE.

关键词： Contrastive Learning

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Blockchain-Driven Academic Learning Record Management in Higher Education: A Comprehensive Review of Methodologies, Applications, Benefits, and Challenges

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SN computer science 2025年第5期6卷 1-33页

作者： Chaudhari, Sheetal Shirole, Mahesh Computer Science and Engineering Department Sardar Patel Institute of Technology Andheri Maharashtra Mumbai 400058 India Computer Engineering and Information Technology Department Veermata Jijabai Technological Institute Matunga Maharashtra Mumbai 400031 India

Blockchain technology has the potential to revolutionize the education sector by improving the legitimacy and security of academic procedures. The academic learning record management records the learner’s academic qualifications and achievements. Keeping track of higher education academic learning records is essential to assess students’ credentials and advance their careers. The COVID-19 outbreak highlighted the need for flexible and collaborative learning systems. However, the processes of recording credit accumulation, verification, and transfer are complicated by the disparate credit schemes found in different universities. This creates the need for an academic credit management system. Secure learning record management is necessary because validating the professional and academic accomplishments listed on the resumé currently poses a challenge. Blockchain can remedy this scenario by guaranteeing data privacy, reducing the possibility of hacking, and offering transparent, traceable academic credentials as well as verification of other achievements. Despite these benefits, challenges are still associated with implementing blockchain in academic learning record management. These include handling multiple credentials with a unique identity, absence of smart contracts, on-chain and centralized storage, scalability problems, transaction costs, restricted control over student data, and inadequate student data recording standards. The main objective of this review is to examine recent studies on blockchain-driven academic learning record management by categorizing them into learning record management and academic credit management. This paper identifies research gaps and suggests further studies and practical applications to enhance transparency, efficiency, and trust in the education system. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Academic bank of credits Academic credit management Blockchain Education Learning record management NEP 2020

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Enhanced Object Detection Based on YOLOv9 for Marine Life 16

Enhanced Object Detection Based on YOLOv9 for Marine Life

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Pawar, Akshat Handa, Ankush Pujari, Madhav Hema, D. Deva Srm Institute of Science and Technology Ramapuram Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331505264

Object detection for marine life leverages advanced computer vision techniques to identify and monitor various marine species and their behaviors in underwater environments. This technology enables efficient data collection, which is crucial for conservation efforts and the study of marine ecosystems. By automating the analysis of underwater imagery and video, researchers can better understand marine biodiversity and respond to environmental changes. However, existing methods for marine life detection often face challenges such as low visibility, varying lighting conditions, and occlusions caused by underwater debris or other marine creatures. Furthermore, these approaches' accuracy and scalability are limited by the time-consuming and error-prone nature of manual dataset processing. The lack of robust datasets and domain-specific models further hampers the effectiveness of traditional methods. Our project seeks to overcome these constraints by creating an enhanced object detection based on YOLO v9 that is more resilient and environment-specific. By incorporating advanced deep learning algorithms and enhancing training with diverse, highquality datasets, our approach improves accuracy and resilience against common underwater challenges. This advancement will streamline the identification process, enabling more precise and scalable marine life monitoring. © 2024 IEEE.

关键词： Invertebrates

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Fixed-time-synchronized control: a system-dimension-categorized approach

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science China(Information sciences) 2023年第7期66卷 177-194页

作者： Wanyue JIANG Shuzhi Sam GE Dongyu LI Institute for Future & Shandong Key Laboratory of Industrial Control Technology School of Automation Qingdao University Department of Electrical and Computer Engineering National University of Singapore School of Cyber Science and Technology Beihang University Shanghai Institute of Satellite Engineering

This study addresses the fixed-time-synchronized control problem of perturbed multi-input multioutput(MIMO) systems. In the task of fixed-time-synchronized control, different dimensions of the output signal in MIMO systems are required to reach the desired value simultaneously within a fixed time *** MIMO system is categorized into two cases: the input-dimension-dominant and the state-dimensiondominant cases. The classification is defined according to the dimension of system signals and, more importantly, the capability of converging at the same time. For each kind of MIMO system, sufficient Lyapunov conditions for fixed-time-synchronized convergence are explored, and the corresponding robust sliding mode controllers are designed. Moreover, perturbations are compensated using the super-twisting technique. The brake control of the vertical takeoff and landing aircraft is considered to verify the proposed method for the input-dimension-dominant case, which shows the essential advantages of decreasing the energy consumption and the output trajectory length. Furthermore, comparative numerical simulations are performed to show the semi-time-synchronized property for the state-dimension-dominant case.

关键词： fixed-time-synchronized convergence sliding mode control perturbed MIMO systems

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