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YOLOv8-Based Frameworks for Liver and Tumor Segmentation Task on LiTS

SN computer science 2024年第6期5卷 741页

作者： Randar, Shyam Shah, Vedanshi Kulkarni, Harshmohan Suryawanshi, Yash Joshi, Amit Sawant, Suraj Department of Computer Science and Engineering School of Computational Science COEP Technological University Pune

Accurate liver tumor diagnosis in clinical practice relies on precisely delineating the liver and identifying potential tumors in Computed Tomography scans. This study aims to develop a lightweight liver and tumor segmentation model, balancing computational cost and accuracy. Traditional methods use Convolutional Neural Networks that enhance the performance but heighten the computational costs. Addressing it, this study presents three frameworks: Sim-Seg, Seg-Seg, and BB-Seg for liver and liver tumor segmentation tasks, leveraging the You Look Only Once network. The proposed Sim-Seg framework achieves a dice score of 89.54% for liver segmentation, while the BB-Seg framework attains a dice score of 80.55% for tumor segmentation on the Liver Tumor Segmentation Benchmark. In conclusion, this study effectively balances computational resources and accuracy, achieving results comparable to those of more compute-intensive methods. It aims to advance the development of YOLO-based frameworks in the medical field. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Healthcare Liver tumor segmentation Medical image segmentation Object detection YOLOv8

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Hybrid RMDL-CNN for speech recognition from unclear speech signal

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International Journal of Speech Technology 2025年第1期28卷 195-217页

作者： Bhargava, Raja Arivazhagan, N. Babu, Kunchala Suresh Research Scholar Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering Potti Sriramulu Chaluvadi Mallikarjuna Rao college of Engineering and Technology Andhra Pradesh Vijayawada India

ASR is an effectual approach, which converts human speech into computer actions or text format. It involves extracting and determining the noise feature, the audio model, and the language model. The extraction and determination of the noise feature is a crucial aspect of speech recognition, serving as both a process of information compression and signal deconvolution. ASR schemes are mostly employed in smart homes, smart appliances, and biometric schemes. Yet, traditional approaches offer very low performance because of a noisy environment. Moreover, local differences and accents negatively influence the ASR scheme execution during the conversion of the speech signals. This paper introduces a hybrid RMDL-CNN method to address these challenges. At first, the input of unclear speech is carried out by the dataset. Then, signal pre-processing is done by employing a Gaussian filter. After that, voice enhancement is accomplished by employing nonlinear spectral subtraction. Later, the speech word is segmented from the enhanced output based on the Attentional Encoder-Decoder approach and finally, the speech is recognized using the proposed RMDL-CNN. The RMDL-CNN method is devised by the combination of RMDL and CNN. Furthermore, the established RMDL-CNN is accessed for its efficiency based on several values of k-group value, as well as learning data. In addition, the introduced RMDL-CNN approach for speech recognition achieved better accuracy, PPV, as well as NPV of 0.909, 0.947, and 0.917 for dataset 1. Moreover, the RMDL-CNN has achieved the highest accuracy of 0.909, PPV of 0.926 and NPV of 0.888 for dataset 2. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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A Graph-Assisted Digital-Twin-Driven Multiagent Shared Offloading for Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17349-17363页

作者： Zahangir Alam, Md Rahman, Suryaia Asif Bin Khaled, Md Islam, Ashraful Jamalipour, Abbas Independent University Bangladesh Department of Computer Science and Engineering Center for Computational and Data Sciences Dhaka1229 Bangladesh The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

Vehicular edge computing (VEC) allows vehicles to process part of the tasks locally at the network edge while offloading the rest of the tasks to a centralized cloud server for processing. A massive volume of tasks generated by the Internet of Vehicles (IoV) leads to buffer overflow that causes higher latency. Elevating latency, in turn, can increase network energy consumption. Both higher latency and energy consumption lead to a degradation of network performance. Therefore, VEC design requires a balance between latency and energy consumption tradeoff. To reduce overwhelming amount of offloading to edge servers, a cooperative cluster-based shared offloading strategy has been proposed in this work. We use digital twin technology in VEC for managing and adapting to environmental dynamic changes. Then, we leverage Lyapunov (Ly) optimization to transform the stochastic offloading problem into a more manageable deterministic form. Finally, we present a decentralized coordination graph (CG)-driven Ly-based multiagent deep deterministic policy gradient (CG-LyMADDPG) algorithm that trains agents toward energy efficient optimal offloading policy while maintaining queue stability at a maximum delay constraint. The experimental result shows that the proposed learning significantly outperforms the baseline algorithms for energy savings while maintain queue stability. © 2014 IEEE.

关键词： Stochastic systems

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Lexicon-based fine-tuning of multilingual language models for low-resource language sentiment analysis

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1116-1125页

作者： Vinura Dhananjaya Surangika Ranathunga Sanath Jayasena Department of Computer Science and Engineering University of MoratuwaMoratuwaSri Lanka School of Mathematical and Computational Sciences Massey UniversityPalmerston NorthNew Zealand

Pre-trained multilingual language models (PMLMs) such as mBERT and XLM-R have shown good cross-lingual transferability. However, they are not specifically trained to capture cross-lingual signals concerning sentiment words. This poses a disadvantage for low-resource languages (LRLs) that are under-represented in these models. To better fine-tune these models for sentiment classification in LRLs, a novel intermediate task fine-tuning (ITFT) technique based on a sentiment lexicon of a high-resource language (HRL) is introduced. The authors experiment with LRLs Sinhala, Tamil and Bengali for a 3-class sentiment classification task and show that this method outperforms vanilla fine-tuning of the PMLM. It also outperforms or is on-par with basic ITFT that relies on an HRL sentiment classification dataset.

关键词： deep learning natural languages natural language processing

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Recent advances in multipactor physics and mitigation

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High Voltage 2023年第6期8卷 1095-1114页

作者： Asif Iqbal De‐Qi Wen John Verboncoeur Peng Zhang Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMichiganUSA Department of Computational Mathematics Scienceand EngineeringMichigan State UniversityEast LansingMichiganUSA

Recent progress made in the prediction,characterisation,and mitigation of multipactor discharge is reviewed for single‐and two‐surface ***,an overview of basic concepts including secondary electron emission,electron kinetics under the force law,multipactor susceptibility,and saturation mechanisms is provided,followed by a discus-sion on multipactor mitigation *** strategies are categorised into two broad areas–mitigation by engineered devices and engineered radio frequency(rf)*** approach is useful in different *** advances in multipactor physics and engineering during the past decade,such as novel multipactor prediction methods,un-derstanding space charge effects,schemes for controlling multipacting particle trajec-tories,frequency domain analysis,high frequency effects,and impact on rf signal quality are *** addition to vacuum electron multipaction,multipactor‐induced ioni-zation breakdown is also reviewed,and the recent advances are summarised.

关键词： multip kinetics susceptibility

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Appearance and motion-based unusual crowd events detection using multiple moving objects

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Multimedia Tools and Applications 2024年第42期83卷 90177-90189页

作者： Babu, K. Atlas, L. Godlin Sreeji, S. Arjun, K.P. Rasmi, A. Senthilvel, P. Gururama Computational Intelligence SRM Institute of Science & Technology Chengalpet Kattankulathur603203 India Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering Gitam University Karnataka Bengaluru India Information Science and Engineering RRIT Bangalore India Saveetha University Chennai602 105 India

Unusual crowd analysis is an important problem in surveillance video due to their features cannot be extracted efficiently on the crowd scenes. To overcome this challenge, this paper introduced the appearance and motion based unusual events detection method by using multiple moving objects. This method includes appearance and motion based computation, and moving objects trajectory analysis. By using these all techniques, the unusual crowds and their activities can be obtained by analyzing moving objects size and the distance among multiple moving objects. Two benchmark datasets such as PETS 2009 and UMN are used for the experimental results. Also, several existing algorithms are compared with the proposed method to evaluate the effectiveness of the proposed work. The comparative analysis results proved that the proposed method achieved best accuracy for unusual crowd events. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： And trajectory analysis Appearance based analysis Distance based analysis Unusual crowd events detection

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HoLens:A visual analytics design for higher-order movement modeling and visualization

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computational Visual Media 2024年第6期10卷 1079-1100页

作者： Zezheng Feng Fang Zhu Hongjun Wang Jianing Hao Shuang-Hua Yang Wei Zeng Huamin Qu Department of Computer Science and Engineering The Hong Kong University of Science and TechnologyHong KongChina Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China Thrust of Computational Media and Arts(CMA) The Hong Kong University of Science and Technology(Guangzhou)Guangzhou 511458China Department of Computer Science University of ReadingBerkshire RG66AHUK Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet Southern University of Science and TechnologyShenzhen 518055China

Higher-order patterns reveal sequential multistep state transitions,which are usually superior to origin-destination analyses that depict only first-order geospatial movement *** methods for higher-order movement modeling first construct a directed acyclic graph(DAG)of movements and then extract higher-order patterns from the ***,DAG-based methods rely heavily on identifying movement keypoints,which are challenging for sparse movements and fail to consider the temporal variants critical for movements in urban *** overcome these limitations,we propose HoLens,a novel approach for modeling and visualizing higher-order movement patterns in the context of an urban *** mainly makes twofold contributions:First,we designed an auto-adaptive movement aggregation algorithm that self-organizes movements hierarchically by considering spatial proximity,contextual information,and tem-poral ***,we developed an interactive visual analytics interface comprising well-established visualization techniques,including the H-Flow for visualizing the higher-order patterns on the map and the higher-order state sequence chart for representing the higher-order state *** real-world case studies demonstrate that the method can adaptively aggregate data and exhibit the process of exploring higher-order patterns using *** also demonstrate the feasibility,usability,and effectiveness of our approach through expert interviews with three domain experts.

关键词： data visualization movement modeling state sequence visualization movement visualization urban visual analytics

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Secure communication routing and attack detection in UAV networks using Gannet Walruses optimization algorithm and Sheppard Convolutional Spinal Network

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Peer-to-Peer Networking and Applications 2024年第5期17卷 3269-3285页

作者： Renu, Yuvaraj Sarveshwaran, Velliangiri Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chengalpattu India Department of Computer Science and Information Engineering National Chung Cheng University Minxiong Chiayi Taiwan

Unmanned Aerial vehicles (UAV) are high-speed moving machines that attained rapid growth in various activities and are considered an integral component in the Satellite-Air -Ground-Sea (SAGS) incorporated network. However, UAVs are affected by communication delays and malicious attacks. Therefore, an adequate and secure communication routing and attack detection model is necessary for UAV communication networks. This research described a novel approach for initiating secure communication in UAV networks namely Gannet Walruses Optimization Algorithm + Sheppard Convolutional Spinal Network (GWOA + ShCSpinalNet). Initially, the UAV network is simulated, and the data packets are transmitted among the nodes using optimal routing paths. An optimal routing path is computed using the Gannet Walruses Optimization Algorithm (GWOA) by considering some multi-objective functions through the Deep Recurrent Neural Network (DRNN). The developed GWAO integrates Gannet Optimization (GOA) and Walruses Optimization (WaOA). The data communication is done through monitoring agents. The newly devised Sheppard Convolutional Spinal Network (ShCSpinalNet) is utilized as a decision-making agent for malicious attack detection. The attributes considered for decision-making are round trip time, packet delivery ratio, the strength of the signal, the size of the packet, and the number of incoming packets. Once the SpinalNet categorizes the normal and attacked nodes the defense agent is implemented for attack migration. The ShCSpinalNet is devised by the combination of the Sheppard Convolutional Neural Network and Spinal Network. The GWOA + ShCSpinalNet accomplishes a diminished delay of 0.614 s, an increased detection rate of 0.930%, an energy of 0.439 J, and a Packet Delivery Ratio (PDR) of 0.749. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Recurrent neural networks

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Gender classification based on handwriting using Gurmukhi characters and hybrid feature extraction techniques

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Multimedia Tools and Applications 2024年 1-19页

作者： Singla, Chinu Maini, Raman Kumar, Munish Department of Computer Science and Engineering Punjabi University Patiala Punjab Patiala India Department of Computational Sciences Maharaja Ranjit Singh Punjab Technical University Punjab Bathinda India

Gender classification using one or more features has emerged as a promising solution to issues in fields such as criminal and forensic examinations. However, predicting an individual’s gender based on their handwriting remains a challenging task. In this study, the authors propose a novel approach to classify gender based on pre-segmented Gurmukhi (Punjabi) characters. They employ feature extraction methods and classification algorithms such as K-nearest neighbours, random forest, Support Vector Machine, and Multilayer Perceptrons. The dataset consists of approximately 84,000 Gurmukhi characters, contributed by 280 writers, including 150 males and 130 females. Each writer provided 300 characters (60 characters written five times). The authors extracted features based on diagonal, transition, zoning, and peak extent. By using a hybrid of feature extraction techniques, the random forest classifier achieved higher accuracy than the other classifiers. This study presents a promising approach to gender classification based on handwriting and underscores the potential of hybrid feature extraction techniques to improve the accuracy of gender classification systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Enhancing Processor Performance using Bidirectional LSTM Memory Prefetching

Enhancing Processor Performance using Bidirectional LSTM Mem...

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2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Gade, Rachana Bornare, Aditya Jog, Saurabh Joshi, Amit Sawant, Suraj Department of Computer Science and Engineering School of Computational Sciences COEP Technological University Pune India

ISBN: (纸本)9798331533137

Memory prefetching plays a vital role in enhancing processor performance, with modern processors using various prefetching methods to manage data access patterns efficiently. Traditional prefetchers work well with predetermined patterns but struggle with the unpredictability of modern applications. Advanced techniques, especially those employing machine learning, aim to predict and prefetch data based on complex spatiotemporal patterns, yet they face significant challenges like the class explosion problem and labeling issues. This study proposes a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model to dynamically adapt to intricate access patterns, addressing these limitations by leveraging features, localization, and semantic locality to improve prediction accuracy and adaptability. By integrating insights from traditional and machine learning-based approaches, this model seeks to optimize data prefetching, advancing memory system efficiency and ultimately enhancing overall processor performance. These contributions provide a robust framework for designing adaptive and efficient processors that can meet the demands of modern computing environments. ©2024 IEEE.

关键词： Deep learning

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