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12th International Conference on Current Trends in Advanced Computing, ICCTAC 2024

作者： Choudhary, Kartik Singh, Kaptan Saxena, Amit Computer Science & Engineering Truba Institute of Engineering & Information Technology Bhopal India

ISBN: (纸本)9798350395808

Brain tumor diagnosis and treatment need segmentation, Precision automated segmentation of brain tumors is difficult due to their size, shape, unexpected placements, and fuzzy boundaries. U-Net is a popular medical picture segmentation model due to its simple architecture and high performance. Underutilized contextual information, limited local receptive fields, and lost spatial information are issues. For brain tumor segmentation, CDAU-Net, a novel model with Dynamic Convolution and Non-Local Attention Mechanism, addresses these issues. Two-stage cascaded three-dimensional U-Nets rebuild brain tumor spatial information at better resolution. The cascaded network's lateral connections receive maximal attention, improving its ability to collect long-distance interconnections and use tumor context information. Finally, dynamic convolutions with local adaptive capabilities replace normal convolutions in the cascaded network to increase local features capture. Extensive experiments were conducted on publicly available BraTS 2019 and 2020 datasets, comparing the proposed method with other representative approaches. Proposed method segments brain tumors. Brain tumor segmentation dice values of 0.897/0.903 for whole tumor, 0.826/0.828 for tumor core, and 0.781/0.786 for enhanced tumor segmentation worked in the BraTS 2019/2020 validation set. © 2024 IEEE.

关键词： Convolution

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An IoMT-based Federated Learning Survey in Smart Transportation

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Recent Advances in computer science and Communications 2024年第4期17卷 20-39页

作者： Karnam, Geetha Vani Maddikunta, Praveen Kumar Reddy School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a crucial role in enhancing emergency response and reducing the impact of accidents on victims. Smart Transportation uses this technology to improve the efficiency and safety of transportation systems. The current Artificial Intelligence applications lack transparency and interpretability which is of utmost importance in critical transportation scenarios, such as autonomous vehicles, air traffic control systems, and traffic management systems. Explainable Artificial Intelligence (XAI) provides a clear, transparent explanation and actions. Traditional Machine Learning techniques have enabled Intelligent Transportation systems by performing centralized vehicular data training at the server where data sharing is needed, thus introducing privacy issues. To reduce transmission overhead and achieve privacy, a collaborative and distributed machine learning approach called Federated Learning (FL) is used. Here only model updates are transmitted instead of the entire dataset. This paper provides a comprehensive survey on the prediction of traffic using Machine Learning, Deep Learning, and FL. Among these, FL can predict traffic accurately without compromising privacy. We first present the overview of XAI and FL in the introduction. Then, we discuss the basic concepts of FL and its related work, the FL-IoMT framework, and motivations for using FL in transportation. Subsequently, we discuss the applications of using FL in transportation and open-source projects. Finally, we highlight several research challenges and their possible directions in FL. © 2024 Bentham science Publishers.

关键词： Intelligent systems

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Chi-Square Target Encoding for Categorical Data Representation: A Real-World Sensor Data Case Study

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SN computer science 2025年第3期6卷 1-22页

作者： Anitha, M. Savarimuthu, Nickolas Bhanu, S. Mary Saira Department of Computer Applications National Institute of Technology Trichy India Department of Computer Science and Engineering National Institute of Technology Trichy India

Feature engineering is critical for improving machine learning performance (ML), especially when handling categorical data. Traditional encoding methods, such as one-hot and label encoding, often result in challenges like high dimensionality and loss of category significance. To address these limitations, this study introduces a novel Chi-Square Target Encoding (CSTE) approach, which transforms categorical variables into numerical representations by leveraging the chi-square statistic to evaluate the association between categories and the target variable, preserving information effectively. Unlike conventional techniques, CSTE uses Chi-square test values to generate target-based encoded representations, ensuring reliable transformation without information loss. Comprehensive empirical evaluations were conducted on various datasets for binary classification, showcasing CSTE’s superiority over methods like regularized target encoding, basic target encoding, and fuzzification. A case study on real-world sensor data further validated its efficiency and scalability for large-scale data-driven applications. The proposed CSTE method achieved an average increment rate of 3.9447%, surpassing one-hot and fuzzification (1.0430%) and regularized target encoding (1.8308%). Classification outcomes demonstrated F1-scores exceeding 0.90 and AUC values nearing 0.99 across diverse datasets, highlighting its robustness. Furthermore, the reduced dimensionality significantly enhanced inference time while maintaining high accuracy. The CSTE method offers a robust framework for categorical data transformation, addressing limitations of traditional encoding techniques. It improves the interpretability and efficiency of categorical data representation, boosting ML performance. This innovative approach is well-suited for applications across various domains involving categorical data. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Categorical data encoding Chi- Square statistics Data distribution preservation Feature engineering Machine learning Target encoding

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Secure Accelerated Computing: High-Level Synthesis Based Hardware Accelerator Design for CNN Applications 10

Secure Accelerated Computing: High-Level Synthesis Based Har...

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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Chaurasia, Rahul Sengupta, Anirban Indian Institute of Information Technology Department of Computer Science and Engineering Bhopal India Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331533229

This paper introduces a novel methodology for designing secure hardware accelerator tailored for convolutional neural network (CNN) applications, leveraging security-aware high-level synthesis (HLS). The methodology outlined offers comprehensive security measures, including hindrance against reverse engineering (RE) and intellectual property (IP) piracy concerns through architectural transformations and embedded biometric palmprint constraints, respectively. Key contributions of this methodology include: i) robust hindrance against RE attack, ii) robust piracy detection with no additional design overhead, iii) definitive proof of IP ownership by achieving significantly lower probability of coincidence (P), indicating lower probability of false positive, and iv) enhanced tamper tolerance (TT) and resilience against brute force attacks compared to recent methodologies. © 2024 IEEE.

关键词： High level synthesis

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Deep Learning Application in PQRS Segment Detection in ECG Data

Deep Learning Application in PQRS Segment Detection in ECG D...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Pandey, Amit Kumar Gupta, C.L.P. Bansal Institute of Engineering and Technology Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350378092

The electrocardiogram is a vital piece of information about a person's health that provides measurements of changes in the cardiovascular system. When using complex software modules to diagnose diseases, the clinical ECG signal is always tainted by power line interference and electromagnetic fields, leading to inaccurate ***, it's critical to reduce these ECG data acquisition recording inaccuracies in order to perform precise clinical analysis. We are working on an algorithm that will split the data array of three successive ECG signals into three equivalents. The Empirical Mode After decomposing the data matrix and running it through the Independent Component Generation algorithm, we will identify and remove any noisy components using deep learning scheme. To obtain an error-free result of ECG data, we will then apply reverse ICA and EMD features for deep learning. © 2024 IEEE.

关键词： Deep learning

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A hybrid convolutional neural network for sarcasm detection from multilingual social media posts

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Multimedia Tools and Applications 2025年第16期84卷 15867-15895页

作者： Pandey, Rajnish Kumar, Abhinav Singh, Jyoti Prakash Tripathi, Sudhakar Department of Information Technology IET Dr Rammanohar Lohia Awadh University Ayodhya Uttar Pradesh Faizabad India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj211004 India Department of Computer Science and Engineering National Institute of Technology Patna Patna India Department of Information Technology Rajkiya Engineering College Uttar Pradesh Ambedkarnagar India

Sarcasm in social media postings significantly impacts automated sentiment extraction due to its potential to invert the overall polarity of phrases. It poses a formidable challenge in extracting genuine sentiments from informal multilingual comments about events or individuals, especially when the message is in a multilingual form. The prevalence of noisy datasets further complicates this task. This work proposes a hybrid convolutional neural network (CNN-H) model to classify sarcastic and non-sarcastic statements. Unlike traditional CNN models, CNN-H integrates character and word embeddings, enhancing its capacity to handle the complexities of multilingual content. Evaluation on five benchmark datasets, including English (news headlines, Ghosh Tweet, Riloff Tweet), Hindi, and multilingual Hindi-English (Hinglish), demonstrates the superior performance of CNN-H. The model surpasses various state-of-the-art conventional machine-learning and deep-learning models, including CNN, achieving an impressive F1-score of 93.0% on the multilingual dataset. The incorporation of character and word embeddings proves pivotal in mitigating the impact of noisy datasets, prevalent in social media posts. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sentiment analysis

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A Comprehensive Review of Swarm Intelligence-Based Meta-Heuristic Algorithms 3

A Comprehensive Review of Swarm Intelligence-Based Meta-Heur...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Suman, Abhineet Gunjan Biswas, Anupam National Institute of Technology Department of Computer Science and Engineering Delhi 110036 India National Institute of Technology Department of Computer Science and Engineering Assam Silchar788010 India

ISBN: (纸本)9798331519582

Swarm Intelligence (SI) algorithms, motivated by the group actions of natural systems, offer robust and efficient solutions to complex optimization problems. This review explores several key SI algorithms, including Cuckoo Search (CS), Firefly Algorithm (FA), Bat Algorithm (BA), and others. Each algorithm uses ideas from nature, like how ants find food and how bees interact with each other, to effectively solve nonlinear and multimodal problems. Comparative analysis focuses on convergence speed, solution quality, robustness, and computational complexity, highlighting the unique strengths and applicability of each algorithm. SI algorithms can be changed and scaled up or down easily, which makes them useful for dynamic and large-scale optimization tasks. This makes them future-proof solutions that can be used in many areas. Future research directions include new algorithms, hybrid approaches, real-world applications, and integration with machine learning techniques, promising continued advances and broader applicability. The development of SI algorithms is driven by their natural inspiration, simplicity, and potential for innovative applications, underscoring their importance in tackling diverse and complex challenges. © 2025 IEEE.

关键词： Learning algorithms

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AgriAI: Revolutionizing Agriculture with Data science 2

AgriAI: Revolutionizing Agriculture with Data Science

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Raut, Nitin B. Ashwin, U. Aswin, S. Bala Selva Vishnu, N. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350365269

In this ambitious agricultural project, cutting-edge technologies such as Machine Learning (ML) and Deep Learning (DL) are harnessed to revolutionize farming practices in India. The primary focus lies in automating vital tasks like crop disease detection, crop recommendation, and fertilizer suggestion, which are traditionally laborious and time-consuming for farmers. Leveraging recent advancements in ML algorithms, a comprehensive solution is envisaged. This innovative approach involves the integration of sophisticated models into a user-friendly website, creating a seamless user experience. Through this platform, farmers gain access to automated tools that significantly enhance their crop management strategies. The platform provides a comprehensive solution by combining deep learning-based crop disease detection mechanisms, intelligent crop recommendation systems, and effective fertilizer recommendations derived from machine learning algorithms. This combination of technologies helps farmers maximize their overall crop yields and minimize losses caused by diseases in addition to streamlining farming procedures. The synergy of ML, and DL on a unified web interface stands as a testament to the transformative power of technology in agriculture, marking a pivotal step toward sustainable and efficient farming practices in India. © 2024 IEEE.

关键词： Support vector machines

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FusionEXNet: an interpretable fused deep learning model for skin cancer detection

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International Journal of computers and Applications 2024年第9期46卷 743-753页

作者： Gautam, Yuvika Gupta, Piyush Kumar, Deepika Kalra, Bhavya Kumar, Abhinav Hemanth, Jude D. Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Electronics & Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India

Skin cancer poses a significant burden on mankind and healthcare systems globally, necessitating the development of effective diagnostic and treatment strategies. This paper introduces FusionEXNet, an innovative and interpretable fused deep-learning model for skin cancer detection. FusionEXNet leverages the strengths of EfficientNetV2S and XceptionNet architectures to extract robust features from dermoscopic images, achieving superior performance compared to individual models. While XceptionNet and EfficientNetV2S attained accuracies of 88.82% and 88.01%, respectively, FusionEXNet surpassed them with an impressive accuracy of 90.83%. To enhance model interpretability, Explainable Artificial Intelligence (XAI) techniques, such as SmoothGrad and Faster Score-CAM, are integrated, providing valuable insights into the decision-making process. The model was trained and evaluated using the extensive HAM10000 dataset, consisting of over 10,000 high-resolution images across seven skin lesion categories. The proposed FusionEXNet model offers a reliable, accurate, and interpretable system for skin cancer detection, contributing to improved patient outcomes and more efficient diagnostic processes. This study underscores the potential of combining advanced CNN architectures with XAI methods to create powerful tools for dermatological diagnostics. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Deep learning

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Designing a Heuristic Based Hybrid CNN with Attention Mechanism for the Effective Classification of Fish Species 5

Designing a Heuristic Based Hybrid CNN with Attention Mechan...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Bhanumathi, M. Arthi, B. Srm Institute of Science and Technology Department of Computer Science and Engineering India Department of Computer Science and Engineering India College of Engineering and Technology Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350321425

In recent days, the population of fish species is enormously increased. The measurement of the total population of the fish species is also a complex task. The population of fishes can be easily identified by its classification of variants. But, the categorization of fishes is an annoying task for marine ecologists and biologists. The fishery activities can be easily managed by calculating the total population and size. Several methods are developed for classifying the fish species, but they take more computational time and manpower. These limitations are overthrown by automatic methods. The automatic method monitors the activities of fish and their path. The major advantages of this automatic System are to reduce labor requirement and time. This research study suggests a new fish species categorization model with the help of a Hybrid Convolution Neural Network(CNN) with an Attention Mechanism (HCNN-AM). In the initial phase, the images are aggregated from the standard benchmark dataset. Then, the images are forwarded to the hybrid CNN with an attention mechanism for effectively classifying the fish species, in which the attributes are optimized with the aid of a hybrid Fitness-based Hybrid Rat Swarm with Billiards-inspired Optimization (FH-RSBO). Therefore, the planned method is an efficient substitute for strenuous and time-consuming methods of physical recognition by marine experts. Thus, it becomes the most advantageous task to monitor the fish biodiversity in their natural habitats. © 2023 IEEE.

关键词： Optimization

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