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1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Khan, Andleeb Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Department of Computer Science and Engineering Sharda University Greater Noida India Department of Computer Engineering Istinye University Istanbul34396 Turkey Department of Computer Science National University of Computer and Emerging Sciences Islamabad44000 Pakistan

ISBN: (纸本)9783031823763

With the advancing technology, the increase in threats has been exponential. These technologies have led to the production of huge amounts of network traffic data. Therefore, it is of immense importance for the companies to safeguard this sensitive data from any attack by the implementation of robust security techniques as this can cause heavy financial losses to the companies. There is need for the enhancement in the performance of existing Intrusion Detection Systems in order to detect the attacks more diligently. This is done by the integration of Machine Learning in the intrusion detection approaches as they develop models which are data driven and hence detect and prevent cyber-attacks. This study develops an ML based intrusion detection model by using Random Forest, Naïve Bayes and XGBoost classifiers on UNSW_NB15 dataset and pre-processing the dataset by applying One Hot Encoder and thereby giving an accuracy of 99.98%, 74.59% and 66.20% respectively. The performance evaluation indicates that the Random Forest model with an accuracy of 99.98% clearly outperforms the other algorithms. Hence, this framework can improve the system security to a greater extent. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network intrusion

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GRA: Graph Representation Alignment for Semi-Supervised Action Recognition

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IEEE Transactions on Neural Networks and Learning Systems 2024年第9期35卷 11896-11905页

作者： Huang, Kuan-Hung Huang, Yao-Bang Lin, Yong-Xiang Hua, Kai-Lung Tanveer, M. Lu, Xuequan Razzak, Imran National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei10607 Taiwan Indian Institute of Technology Indore Department of Mathematics Indore Simrol453552 India La Trobe University Department of Computer Science and IT MelbourneVIC3086 Australia University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia

Graph convolutional networks (GCNs) have emerged as a powerful tool for action recognition, leveraging skeletal graphs to encapsulate human motion. Despite their efficacy, a significant challenge remains the dependency on huge labeled datasets. Acquiring such datasets is often prohibitive, and the frequent occurrence of incomplete skeleton data, typified by absent joints and frames, complicates the testing phase. To tackle these issues, we present graph representation alignment (GRA), a novel approach with two main contributions: 1) a self-training (ST) paradigm that substantially reduces the need for labeled data by generating high-quality pseudo-labels, ensuring model stability even with minimal labeled inputs and 2) a representation alignment (RA) technique that utilizes consistency regularization to effectively reduce the impact of missing data components. Our extensive evaluations on the NTU RGB+D and Northwestern-UCLA (N-UCLA) benchmarks demonstrate that GRA not only improves GCN performance in data-constrained environments but also retains impressive performance in the face of data incompleteness. © 2012 IEEE.

关键词： Alignment

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Handwritten Digit Recognition using Ensemble Learning with Deep Learning-based Feature Fusion 8

Handwritten Digit Recognition using Ensemble Learning with D...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Ankoliya, Arjav Bhadani, Hetvi Dalsania, Hitwanshi Goel, Parth Computer Engineering Department Anand Changa India Computer Science & Engineering Department Anand Changa India

ISBN: (纸本)9798350376425

India with its linguistic diversity consists of 22 officially recognized languages. The multilingual nation is shifting towards digitization which has brought an upsurge in identification of handwritten digits in regional languages. Extensive work has been done for Hindi, Bangla and Gurumukhi language. However, less efforts have been made for Gujarati handwritten numerals classification. Moreover, the diversity of writing styles and the complex curves makes it even more challenging to distinguish handwritten data as compared to printed data. This paper aims to present the ensemble-based feature extraction from deep learning models inclusive of ResNet50, VGG-19, VGG-16, InceptionV3 and utilizing XGboost for classification. Comparative analysis of several models and proposed framework has been provided. Experimental result illustrates that the proposed method with fused features obtained the highest testing accuracy of 99.68% among the four individual deep learning models. © 2024 IEEE.

关键词： Federated learning

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Task offloading delay minimization in vehicular edge computing based on vehicle trajectory prediction

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Digital Communications and Networks 2025年第2期11卷 537-546页

作者： Feng Zeng Zheng Zhang Jinsong Wu School of Computer Science and Engineering Central South UniversityChangsha410083China School of Artificial Intelligence Guilin University of Electronic TechnologyGuilin541000China The Department of Computer Science Universidad de ChileSantiagoChile

In task offloading,the movement of vehicles causes the switching of connected RSUs and servers,which may lead to task offloading failure or high service *** this paper,we analyze the impact of vehicle movements on task offloading and reveal that data preparation time for task execution can be minimized via forward-looking ***,a Bi-LSTM-based model is proposed to predict the trajectories of *** service area is divided into several equal-sized *** the actual position of the vehicle and the predicted position by the model belong to the same grid,the prediction is considered correct,thereby reducing the difficulty of vehicle trajectory ***,we propose a scheduling strategy for delay optimization based on the vehicle trajectory *** the inevitable prediction error,we take some edge servers around the predicted area as candidate execution servers and the data required for task execution are backed up to these candidate servers,thereby reducing the impact of prediction deviations on task offloading and converting the modest increase of resource overheads into delay reduction in task *** results show that,compared with other classical schemes,the proposed strategy has lower average task offloading delays.

关键词： Vehicular edge computing Task offloading Vehicle trajectory prediction Delay minimization Bi-LSTM model

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Speaker Verification System Based on Time Delay Neural Network with Pre-activated CNN Stem and Deep Layer Aggregation

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Journal of Information science and engineering 2024年第2期40卷 303-316页

作者： Lin, Wei-Ting Chen, Ting-Wei Chen, Chia-Ping Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung804 Taiwan

In this paper, we improve the state-of-the-art ECAPA-TDNN model for speaker verification with CNN stem, self-calibration (SC) block, and deep layer aggregation. The proposed architecture is called Emphasized Channel Attention Propagation and Deep Layer Aggregation with Pre-activated CNN Stem in Time Delay Neural Network, which is abbreviated as ECAPDLA CNNv2-TDNN. First, we add a pre-activated stemming convolution layer in front of the main ECAPA-TDNN architecture. This ensures that the input to our main model architecture is a stable feature representation. Next, we change the multi-layer aggregation of ECAPA-TDNN to deep layer aggregation and replace the SE-Res2block in ECAPA-TDNN with SC block. Thus, the proposed implementation enhances feature extraction on multiple time scales and spectral channels and improves the overall training efficacy. On the VoxCeleb1-O dataset, the proposed model achieves an equal error rate (EER) of 0.95%. This is significantly better than the EER of 1.23% achieved by the ECAPA-TDNN baseline. © 2024 Institute of Information science. All rights reserved.

关键词： Time delay

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A Comprehensive Analysis of Human Action Recognition for Noisy Videos Using CNN and LSTM

A Comprehensive Analysis of Human Action Recognition for Noi...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Suma, S. Patel, Punyaban Tiwari, Rajesh Gundu, Lavanya Devi, Guntoju Kalpana Raju, K. Srujan Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering & Technology Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Computer Science and Engineering CMR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798331505134

Human action identification has advanced significantly as a result of the development of deep learning algorithms. Convolutional Neural Networks (CNNs) are known for their adeptness at extracting crucial information within video frames, especially shapes and postures. However, understanding movements necessitates not just individual frames as well as the temporal flow of the movement;this is wherein LSTM (long short-term memory) variants of Recurrent Neural Networks (RNNs) come into play. By analyzing frame sequences, LSTMs are particularly effective in gathering temporal connections, recalling prior motions, and overcoming the gap amongst static features as well as dynamic actions. Even with CNNs and LSTMs' remarkable accuracy in human action recognition (HAR), challenges remain. © 2024 IEEE.

关键词： Convolutional neural networks

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A Novel Insertion Solution for the Travelling Salesman Problem

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computers, Materials & Continua 2024年第4期79卷 1581-1597页

作者： Emmanuel Oluwatobi Asani Aderemi Elisha Okeyinka Sunday Adeola Ajagbe Ayodele Ariyo Adebiyi Roseline Oluwaseun Ogundokun Temitope Samson Adekunle Pragasen Mudali Matthew Olusegun Adigun Department of Computer Science Landmark UniversityOmu Aran251103Nigeria SDG 11 Group Landmark UniversityOmu Aran251103Nigeria Department of Computing MiVA UniversityAbuja900211Nigeria Department of Computer Science Ibrahim Badamasi Babangida UniversityLapai911101Nigeria Department of Computer Science University of ZululandKwadlangezwa3886South Africa Department of Computer&Industrial Production Engineering First Technical UniversityIbadan200243Nigeria Department of Multimedia Engineering Kaunas University of TechnologyKaunasLT-44249Lithuania Department of Computer Science Colorado State UniversityFort Collins80523USA

The studypresents theHalfMax InsertionHeuristic (HMIH) as a novel approach to solving theTravelling SalesmanProblem (TSP). The goal is to outperform existing techniques such as the Farthest Insertion Heuristic (FIH) andNearest Neighbour Heuristic (NNH). The paper discusses the limitations of current construction tour heuristics,focusing particularly on the significant margin of error in FIH. It then proposes HMIH as an alternative thatminimizes the increase in tour distance and includes more nodes. HMIH improves tour quality by starting withan initial tour consisting of a ‘minimum’ polygon and iteratively adding nodes using our novel Half Max *** paper thoroughly examines and compares HMIH with FIH and NNH via rigorous testing on standard TSPbenchmarks. The results indicate that HMIH consistently delivers superior performance, particularly with respectto tour cost and computational efficiency. HMIH’s tours were sometimes 16% shorter than those generated by FIHand NNH, showcasing its potential and value as a novel benchmark for TSP solutions. The study used statisticalmethods, including Friedman’s Non-parametric Test, to validate the performance of HMIH over FIH and *** guarantees that the identified advantages are statistically significant and consistent in various situations. Thiscomprehensive analysis emphasizes the reliability and efficiency of the heuristic, making a compelling case for itsuse in solving TSP issues. The research shows that, in general, HMIH fared better than FIH in all cases studied,except for a few instances (pr439, eil51, and eil101) where FIH either performed equally or slightly better thanHMIH. HMIH’s efficiency is shown by its improvements in error percentage (δ) and goodness values (g) comparedto FIH and NNH. In the att48 instance, HMIH had an error rate of 6.3%, whereas FIH had 14.6% and NNH had20.9%, indicating that HMIH was closer to the optimal solution. HMIH consistently showed superior performanceacross many benchmarks,

关键词： Nearest neighbour heuristic farthest insertion heuristic half max insertion heuristic tour construction travelling salesman problem

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Efficient disease identification using symptom-based ensemble models and bayes-search optimization

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9663-9676页

作者： Indupalli, Manjula Rani Pradeepini, Gera Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Symptom-based disease identification is crucial to the diagnosis of the disease at the early stage. Usage of traditional stacking and blending models i.e., with default values of the models cannot handle the multi-classification data properly. Some of the existing researchers also implemented tuning with the help of a grid search approach but it consumes more time because it checks all the possible combinations. Suppose if the model has n estimators with k values it needs to check (n*k)! elements combination, this makes the learning time high. The proposed model chooses the estimators to train the model with in a considerable amount of time using an advanced tuning technique known as "Bayes-Search" on an ensemble random forest and traditional, support vector machine. The advantage of this model is its capability to store the best evaluation metrics from the previous model and utilise them to store the new model. This model chooses the values of the estimator based on the probability of selection, which reduces the elements in search space i.e., ( © 2024 – IOS Press.

关键词： Support vector machines

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Deep learning framework for automatic detection and classification of sleep apnea severity from polysomnography signals

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Neural Computing and Applications 2024年第28期36卷 17483-17493页

作者： Raja Brundha, A. Lakshmi Sangeetha, A. Balajiganesh, A. Department of Artificial Intelligence and Data Science Sri Sai Ram Engineering College Chennai India Department of Computer Science Engineering Velammal Engineering College Chennai India

Sleep apnea (SA) is a sleep-related breathing disorder characterized by breathing pauses during sleep. A person’s sleep schedule is significantly influenced by that person’s hectic lifestyle, which may include unhealthy eating habits and their line of work. Polysomnographic (PSG) sleep studies examine sleep-related disorders by recording various biosignals from the human body. However, SA classification methods could be more robust in terms of performance because they rely on feature-engineering strategies or employ a particular signal from PSG recording for diagnosis. This study aims to classify the severity of SA according to the apnea–hypopnea index (AHI) into normal, mild, and moderate-to-severe groups using oxygen saturation (SpO2), electroencephalogram (EEG), and electrocardiogram (ECG) signals. The proposed deep neural network (DNN)-bidirectional long short-term memory (DNN-BiLSTM) framework addresses the issue of low detection accuracy in analysis. The DNN-BiLSTM approach employs features extracted from a multiscale dilation attention 1D convolutional neural network (MSDA-1DCNN) as input for detection and classification purposes. The MSDA-1DCNN network extracts deep features from processed SpO2, EEG, and ECG signals. The developed firefly combined electric fish optimization (FCEFO) algorithm improves performance by optimizing the hidden neuron count of the DNN and the learning rate of the BiLSTM framework. The performance measures proved the effectiveness of the model over conventional machine and deep learning approaches. With the integration of deep features, the proposed DNN-BiLSTM model provides enhanced performance in terms of accuracy and precision. Thus, the proposed approach is progressive and can be used for medical diagnosis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electrocardiograms

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RRSC-128/64-V1: a two-variant simple, fast, and secure lightweight stream cipher

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International Journal of Information Technology (Singapore) 2025年 1-9页

作者： Chatterjee, Runa Chakraborty, Rajdeep Department of Computer Science and Engineering Netaji Subhash Engineering College West Bengal Kolkata 700152 India Department of Computer Science and Engineering Medi-Caps University Madhya Pradesh Indore India

Lightweight cryptography is now emerging as a new method for providing security to resource-constrained devices. Securing those devices from external hackers during data transmission is now becoming an important issue. This paper proposed a lightweight simple stream cipher which provides security and confidentiality of the data among resource-constrained devices. The proposed cipher comes in two variants: 128-bit and 64-bit based on the key size it used. The user can used any of these two variants as per application type and the features of the resources. Although the stream cipher design is simple but it delivers outstanding hardware performance compared to some well-known stream ciphers like Grain, Trivium, Mickey and two Grain-like stream ciphers Planet and Lizard. It proves its efficiency greater than all except Trivium. It also passed the 15 randomness tests of the National Institute of Standards and Technology (NIST). © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Embedded system FPGA Hardware metrics LFSR Lightweight cryptography Lightweight stream cipher NFSR NIST Nonce Randomness Throughput

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