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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Nivethithai, S. Hariharan, B. Srm Institute of Science and Technology Department of Computer Science Kattankulathur India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur India

ISBN: (纸本)9798350379945

Software-defined networking (SDN) offers enhanced control over network infrastructure through a central controller, but it remains vulnerable to security threats like DDoS attacks. This paper proposes an automatic attack detection system for SDN, comprising pre-processing, feature selection using Adaptive Walrus Optimization Algorithm (AWaOA), and classification using the enhanced long short-term memory (ELSTM) model. Pre-processing eliminates redundant and missing data, while feature selection optimizes the important features for classification. The ELSTM model then classifies traffic as malicious or legitimate. Performance evaluation includes metrics like accuracy, precision, and recall to assess the effectiveness of the proposed approach in detecting and mitigating DDoS attacks in SDN environments. © 2024 IEEE.

关键词： Static random access storage

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NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detection Using Neuroimaging Data 4

NeuroAI: A Cascaded CNN Model for High-Accuracy Autism Detec...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Hema, V. Gowri, S. Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331508845

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that impacts social communication, behavior, and cognitive functions. Early detection of autism is crucial for timely intervention, which can significantly improve developmental outcomes for individuals. This paper presents a novel methodology for detecting Autism Spectrum Disorder (ASD) using a Cascaded Convolutional Neural Network (CCNN) model. The proposed CCNN model processes neuroimaging data to detect patterns indicative of autism. The dataset used comprises 6,214 samples from the Autism Brain Imaging Data Exchange (ABIDE). Key preprocessing steps include resizing images to 224x224 pixels, noise reduction using a Median Filter, image cropping to focus on the region of interest (ROI), and contrast enhancement using histogram equalization. The CCNN architecture involves multiple convolutional layers, followed by max-pooling and fully connected layers, culminating in a Softmax layer for classification. The model is trained using the Adam optimizer with binary cross-entropy loss, achieving an accuracy of 93.19% on the test set, outperforming traditional models such as SVM, Random Forest, and Logistic Regression. © 2024 IEEE.

关键词： Median filters

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Deep ensemble model for skin cancer classification with improved feature set

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Multimedia Tools and Applications 2025年第10期84卷 7599-7626页

作者： Burada, Sreedhar Manjunathswamy, B.E. Kumar, M. Sunil Department of Computer Science and Engineering Don Bosco Institute of Technology Karnataka Bengaluru560074 India Department of Computer Science and Engineering School of Computing Mohan Babu University Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati517102 India

Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced models for the accurate classification of skin disease. This paper introduces a novel skin cancer detection model under the stages of preprocessing, feature extraction, classification as well as segmentation. The given input is first preprocessed via Gaussian filtering. Subsequently, the Deep learning model termed as ‘U-net’ is used for the segmentation process. Afterward, the extraction of the feature set including features like Improved Center symmetric Local Binary Pattern (ICS-LBP), Local Gradient Pattern (LGP), Median binary pattern (MBP) is performed. Classification takes place using Deep Ensemble Model based on the extracted features, that includes BiGRU, Deep Maxout, and Improved Deep Convolutional Neural Network (CNN), respectively. Also, as the hyperparameter tuning of the classification model enhances the performance, this work introduces a new Hybrid Atom and Arithmetic Operation Algorithm (HAAOA) algorithm for tuning the optimal weights of the Bi-GRU (Bi-directional Gated Recurrent Unit) and Deep Maxout model. At last, the validation of the proposed work is performed through different performance measures by comparing it with different algorithms. Accordingly, accuracy of the adopted method at 90th learning percentage is 93.56%, which is (~) 82.63%, 85.15%, 84.98%, 83.66% and 83.42% better than AOA, ASO, BWO, BMO and DO, respectively. © 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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Infield disease detection in citrus plants: integrating semantic segmentation and dynamic deep learning object detection model for enhanced agricultural yield

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Neural computing and Applications 2024年第35期36卷 22485-22510页

作者： Rani, N. Shobha Krishna, Arun Sri Sunag, M. Sangamesha, M.A. Pushpa, B.R. University Bengaluru India Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysuru India Department of Chemistry The National Institute of Engineering Mysuru India

Simultaneous and dynamic detection of diseases from infield images of citrus plants is the primary objective of this investigation. Refraining from discarding the disease-related issues would reduce agricultural yield and production, resulting in significant crop loss and economic instability. computer Vision offers an inexpensive and efficient solution for detecting and predicting multiple diseases from infield images of citrus plants in various stages of plant growth. This study aims to perform simultaneous detection and prediction of cankers, mites, and nutritional deficiencies from infield images of citrus plants. For this purpose, over 4914 samples are annotated that are obtained from 441 image samples collected from orchards of citrus plants. Initially, the input image is subject to preprocessing followed by segmentation of prominent leaf regions and disease prediction. Contributions investigated in this work are two-fold: a deep semantic segmentation model named Dynamic U-Net is employed to extract prominent regions of interest, and a dynamic, lightweight object detection deep learning model for predicting diseases. From experimental outcomes, the segmentation efficiency is found to be 89.07% foreground accuracy, 0.7881 of IoU, and 0.9188 of the Dice coefficient. The object detection performance is quantified using the mapped metric, resulting in 0.85, 0.71, 0.64, and 0.27 efficiency concerning plant diseases, cankers, mites, and nutritional deficiency. As per the findings, the proposed approach is an effective solution to perform automated detection and prediction from infield images of citrus plants. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Citrus fruits

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Generative adversarial networks with stochastic gradient descent with momentum algorithm for video-based facial expression

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3703-3722页

作者： Cherian, Aswathy K. Vaidhehi, M. Arshey, M. Briskilal, J. Simpson, Serin V. Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India Department of AI & amp School of Computer Science & amp

Video data is an asset that may be used in various settings, such as a live broadcast on a personal blog or a security camera at a manufacturing facility. Both of these examples are examples of how video data can be used. It is becoming increasingly common practice across a wide range of applications to use a machine learning appliance as a tool for processing video. Recent years have seen significant advancements made in the field of machine learning in computer vision. These advancements have been achieved. The presentation of humans is approached or even surpassed in areas such as item identification, object categorization, and image segmentation. Despite this, challenging difficulties exist, such as identifying human emotions. This study aims to recognize human emotions by analyzing still images and motion pictures taken from motion pictures using numerous machine learning procedures. To accomplish this, neural networks constructed based on Generative Adversarial Networks (GAN) were used to classify each face picture obtained from a frame into one of the seven categories of facial emotions we chose. To communicate feelings, videos are mined for informative aspects such as audio, single, and multiple video frames. During this process stage, separate instances of the OpenSMILE and Inception-ResNet-v2 models extract feature vectors from the audio and frames. After that, numerous classification models are trained using stochastic gradient descent with the impetus approach (SGDMA). The findings from each of the pictures are compiled into a table, and from that, it is determined which facial expression was seen on-screen the most often throughout the film. The classification of audio feature vectors is accomplished with the application of GAN-SGDMA. The Inception-ResNet-v2 algorithm is utilized to recognize feelings conveyed by still photographs. The findings of several experiments suggest that the presented distributed model GAN-SGDMA could significantly boost the sp

关键词： Attention model Face recognition Generative Adversarial Networks (GAN) Stochastic gradient descent with momentum algorithm (SGDMA) Video surveillance

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Paired Transmission in Networks

Paired Transmission in Networks

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International Conference on Computational Engineering, ICCE 2023

作者： Sujana G, Jessy Sharon, Jane Olive Anitha, J. Department of Computer Science SRM Institute of Science and Technology Vadapalani Chennai India Department of Computing Technologies SRM Institute of Science and Technology Chennai India Department of Mathematics Rajalakshmi Institute of Technology Chennai India

ISBN: (纸本)9783031777639

The transmission of a vertex is defined as the sum of the lengths of all shortest paths between the chosen vertex and all other vertices in G. The transmission of a graph G is the sum of the transmissions of all its vertices which is nothing but twice its Wiener index. In location theory, sets of vertices, with the minimum (or maximum) distance in a graph, play a special role because they form target sets for locations of facilities. The framework is made more general by considering the sum of distances to a specified multiset of vertices referred to as profiles. In this paper, we have defined paired transmission of vertices for a network as a special case of profiles, referred to as a paired facility location. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network theory (graphs)

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3D Electromechanical Coupling Simulation Under Heart Failure: Exploring Reentry Phenomena and Arrhythmogenesis

3D Electromechanical Coupling Simulation Under Heart Failure...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Wei Bu, Xianda Li, Qince Wang, Kuanquan Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Harbin Institute of Technology Faculty of Computing Harbin China

ISBN: (纸本)9798350386226

Heart failure alters the electrophysiological properties of cardiomyocytes, leading to changes in the overall mechanical function of the heart, with significant implications for human health. Current research on heart failure predominantly focuses on two-dimensional electrophysiological models, which limits their ability to fully capture the complexity of heart failure. In contrast, our work integrates both electrophysio-logical and mechanical aspects by developing a cardiomyocyte model incorporating heart failure remodeling within a three-dimensional electromechanical coupling model, providing a more comprehensive understanding of heart failure dynamics. We investigated the variations in electromechanical coupling properties of the left ventricle under heart failure conditions, focusing on key indicators such as ventricular action potentials, myocardial contractility, ventricular volume, and pressure. The simulation results successfully reproduced the impact of heart failure on both electrophysiological and mechanical characteristics. Additionally, the simulation observed reentrant waves under heart failure conditions, further revealing that heart failure predisposes the heart to reentrant arrhythmias. In conclusion, the 3D electromechanical coupling model presented in this study offers crucial insights into the mechanisms of reentry phenomena and arrhythmogenesis under heart failure conditions. © 2024 IEEE.

关键词： Heart

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Two Counterexamples to Tokenization and the Noiseless Channel 30

Two Counterexamples to Tokenization and the Noiseless Channe...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Cognetta, Marco Zouhar, Vilém Moon, Sangwhan Okazaki, Naoaki Tokyo Institute of Technology School of Computing Japan ETH Zurich Department of Computer Science Switzerland

ISBN: (纸本)9782493814104

In Tokenization and the Noiseless Channel (Zouhar et al., 2023a), Rényi efficiency is suggested as an intrinsic mechanism for evaluating a tokenizer: for NLP tasks, the tokenizer which leads to the highest Rényi efficiency of the unigram distribution should be chosen. The Rényi efficiency is thus treated as a predictor of downstream performance (e.g., predicting BLEU for a machine translation task), without the expensive step of training multiple models with different tokenizers. Although useful, the predictive power of this metric is not perfect, and the authors note there are additional qualities of a good tokenization scheme that Rényi efficiency alone cannot capture. We describe two variants of BPE tokenization which can arbitrarily increase Rényi efficiency while decreasing the downstream model performance. These counterexamples expose cases where Rényi efficiency fails as an intrinsic tokenization metric and thus give insight for building more accurate predictors. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Efficiency

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Solving the subset sum problem by the quantum Ising model with variational quantum optimization based on conditional values at risk

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science China(Physics,Mechanics & Astronomy) 2024年第8期67卷 43-55页

作者： Qilin Zheng Miaomiao Yu Pingyu Zhu Yan Wang Weihong Luo Ping Xu Institute for Quantum Information and State Key Laboratory of High Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China Hefei National Laboratory Hefei 230088China

The subset sum problem is a combinatorial optimization problem,and its complexity belongs to the nondeterministic polynomial time complete(NP-Complete)*** problem is widely used in encryption,planning or scheduling,and integer *** accurate search algorithm with polynomial time complexity has not been found,which makes it challenging to be solved on classical *** effectively solve this problem,we translate it into the quantum Ising model and solve it with a variational quantum optimization method based on conditional values at *** proposed model needs only n qubits to encode 2ndimensional search space,which can effectively save the encoding quantum *** model inherits the advantages of variational quantum algorithms and can obtain good performance at shallow circuit depths while being robust to noise,and it is convenient to be deployed in the Noisy Intermediate Scale Quantum *** investigate the effects of the scalability,the variational ansatz type,the variational depth,and noise on the ***,we also discuss the performance of the model under different conditional values at *** computer simulation,the scale can reach more than nine *** selecting the noise type,we construct simulators with different QVs and study the performance of the model with *** addition,we deploy the model on a superconducting quantum computer of the Origin Quantum Technology Company and successfully solve the subset sum *** model provides a new perspective for solving the subset sum problem.

关键词： subset sum problem quantum Ising model conditional values at risk variational quantum optimization

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Multi-Task Learning for UAV Trajectory and Caching With Federated Cloud-Assisted Knowledge Distillation

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2516-2533页

作者： Tietaa Maale, Gerald Arden Elorm Kuadey, Noble Arafat, Yeasin Thomas, Kwantwi Sun, Guolin Liu, Guisong Research Institute of Intelligent Computing University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China McCoy University College of Education Department of Mathematics and ICT Nadowli Ghana Research Institute of Intelligent Computing at the University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China Ho Technical University Department of Computer Science Ho Ghana University of Mines and Technology Department of Computer Science and Engineering Tarkwa Ghana Southwestern University of Finance and Economics Complex Laboratory of New Finance and Economics School of Computing and Artificial Intelligence Chengdu610074 China Ministry of Education Engineering Research Center of Intelligent Finance Chengdu610030 China

The proliferation of Internet of Things (IoT) technologies and ubiquitous connectivity has led to uncrewed aerial vehicles (UAVs) playing key role as edge servers, revolutionizing the wireless communications landscape by facilitating computing and caching resources closer to ground users (GUs). This advancement significantly alleviates core network loads, reduces latency, and guarantees content availability even in congested or remote areas. However, jointly optimizing UAV caching strategies and trajectories gives rise to a multi-task optimization (MTO) problem. This paper introduces a novel multi-task geo-temporal caching (MT-GTC) framework that addresses the interplay between UAV caching mechanisms and trajectory optimization in a cohesive manner. Leveraging a proposed multi-task learning (MTL) model for joint optimization of UAV caching and trajectory design, we develop a federated learning cloud-assisted knowledge distillation (FL-CAKD) scheme to preserve data privacy and adapt to data heterogeneity. FL-CAKD transfers knowledge from a cloud model orchestrator (CMO), which houses a large and sophisticated teacher model, to a lightweight on-device MTL student models using soft target distributions instead of large model parameters, significantly reducing communication costs. MT-GTC optimizes caching and trajectories to maximize cache hits and minimize latency. Evaluations on real-world mobility datasets demonstrate up to 95% cache hit rates and 21% lower delays compared to baselines. © 2004-2012 IEEE.

关键词： Multi-task learning

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