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A new hybrid MobileNet-MLP model for secured data communication using signature verification and deep learning

Iran Journal of computer science 2024年第1期8卷 233-252页

作者： Pandiyaraju, V. Ganapathy, Sannasi Thangaramya, K. Nahar, R. Raunak Kannan, A. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department Computer Science and Engineering Education National Institute of Technical Teachers Training and Research (NITTTR) Bhopal India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Signature verification plays a critical role in various industries, including finance and document authentication. However, traditional verification techniques have limitations, such as a lack of robustness and an inability to effectively capture intricate patterns and relationships within the signature images. This paper introduces an innovative approach that addresses the drawbacks by integrating conventional machine learning methods with deep learning techniques. For this purpose, we design a new hybrid MobileNet model using deep learning to extract features from the signature images. Here, the Multilayer Perceptron (MLP) classifier is also used to carry out the binary classification over the extracted features. The experimental results of the proposed model demonstrate that the amalgamated technique surpasses the conventional methods and underscores the possibilities of merging traditional machine learning and deep learning in signature verification. Moreover, the proposed model overcomes the limitations of the traditional signature verification techniques by leveraging the strengths of both deep learning and traditional machine learning. Finally, it obtains a more robust and efficient solution and proves it by providing high security to the data in terms of accuracy. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural network Feature extraction MobileNet Multilayer perceptron Signature verification

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VisionReli6G: Enhancing 6G Wireless Reliability with CNN-LSTM for LOS Blockage Prediction 2024

VisionReli6G: Enhancing 6G Wireless Reliability with CNN-LST...

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16th International Conference on Contemporary Computing, IC3 2024

作者： Bajaj, Sagar Patel, Dhaval K. Raval, Mehul S. Zaveri, Mukesh Merchant, Shabbir N. School of Engineering and Applied Science Ahmedabad University Ahmedabad India Department of Computer Science and Engineering SVNIT Surat India Department of Electrical Engineering IIT Bombay Mumbai India

ISBN: (纸本)9798400709722

The mmWave and sub-THz signals rely on Line-of-Sight (LOS) links for higher throughput. Blocking these links can lead to a sudden drop in the received SNR and increase the latency of the communication network. We propose a CNN-LSTM pipeline designed to enhance the reliability of 6G wireless networks by predicting LOS blockages in advance using a sequential stream of RGB images. The proposed solution's efficiency is demonstrated by comparing key performance indicators between the baseline (YOLO-GRU) and the developed pipelines (ResNet-LSTM, ResNeXt-LSTM). The results are validated on the first real-world 6G communication dataset, DeepSense 6G, which contains diverse data modalities captured at different locations and times of the day. The Top-1 accuracy for future 1, 5, and 10 instances of scenario 22 yields an average increase of 10.63% and 12.60% with the Resnet-LSTM and ResNeXt-LSTM pipelines respectively compared to the baseline YOLO-GRU model. The F1 score of scenarios 17-22 for future-1 time window prediction increased by an average of 7.58% and 8.13% for both pipelines respectively. © 2024 Copyright held by the owner/author(s).

关键词： Phase locked loops

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Parallel Convolutional Neural Network for Brain Tumor Using MR Images 2

Parallel Convolutional Neural Network for Brain Tumor Using ...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Lithika, V. Devi, M.K.Kavitha Thiagarajar College of Engineering Department of Computer Science Engineering Madurai India

ISBN: (纸本)9798350328202

We propose a method that utilizes an Auto-Context Convolutional Neural Network (CNN) to learn important local and global image features from 2D patches of varying window sizes. Brain imaging is a crucial first step in many neuroimaging techniques. The precision and efficiency of brain extraction are important to the overall accuracy. Brain extraction algorithms rely on brain atlases that combine questions about anatomy and assume geometry. However, when these assumptions are violated, the effectiveness of this process is limited. As a result, this study presents a CNN-based automated technique for developing an accurate brain extraction tool. We use two different methods to learn local and global images from two dimension patches: 1) Three parallel 2D convolutions based on three different dimensions (axial, coronal and sagittal). This uses a voxel method of implicitly learning 3D data without computationally expensive 3D convolution. 2 U-net architecture-based advanced convolutional network. We employ network post-processing outputs as interactive data, together with raw pictures, to extract local images and brain connections from non-brain tissue. The study proposes a new model for NLM filtering of MRI images. The proposed technique was tested MRI data. We have achieved outstanding results in brain extraction with our CNN algorithm. It has demonstrated better performance than two recently released public reference sets, LPBA40 and OASIS, with Dice overlap coefficients. Our automated content algorithms have enabled us to make significant improvements. We have also evaluated our algorithm's performance in extracting fetal brains from reconstructed MRI datasets with arbitrary orientation Our CNN surpasses previous approaches in extracting fetal brains from MRI. Our algorithm has achieved a Dice coefficient of 95.97%. Iteratively using posterior probability maps and image patches to extract the brain from non-brain tissue. © 2024 IEEE.

关键词： Brain

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Sandalphon@DravidianLangTech-EACL2024: Hate and Offensive Language Detection in Telugu Code-mixed Text using Transliteration-Augmentation 4

Sandalphon@DravidianLangTech-EACL2024: Hate and Offensive La...

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4th Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, DravidianLangTech 2024

作者： Tabassum, Nafisa Khan, Mosabbir Hossain Ahsan, Shawly Hossain, Jawad Hoque, Mohammed Moshiul Department of Computer Science and Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (纸本)9798891760783

Hate and offensive language in online platforms pose significant challenges, necessitating automatic detection methods. Particularly in the case of codemixed text, which is very common in social media, the complexity of this problem increases due to the cultural nuances of different languages. DravidianLangTechEACL2024 organized a shared task on detecting hate and offensive language for Telugu. To complete this task, this study investigates the effectiveness of transliteration-augmented datasets for Telugu code-mixed text. In this work, we compare the performance of various machine learning (ML), deep learning (DL), and transformer-based models on both original and augmented datasets. Experimental findings demonstrate the superiority of transformer models, particularly Telugu-BERT, achieving the highest f1-score of 0.77 on the augmented dataset, ranking the 1st position in the leader-board. The study highlights the potential of transliteration-augmented datasets in improving model performance and suggests further exploration of diverse transliteration options to address real-world scenarios. © 2024 Association for Computational Linguistics.

关键词： Deep learning

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An Empirical Survey on the Classification Strategies for Automated Alzheimer’s Disease

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SN computer science 2024年第8期5卷 1140页

作者： Mandawkar, Umakant Diwan, Tausif Department of Computer Science and Engineering Indian Institute of Information Technology Nagpur India Department of Computer Engineering SVKM’s Institute of Technology Maharashtra Dhule India

The early Alzheimer’s disease (AD) classification is highly important as this neuro-degenerative disease causes severe problems particularly, loss of memory among the patients. In addition, classifying Normal Control (NC), Mild Cognitive Impairment (MCI) and AD in time assists the patient to undergo treatments in the initial period of the disease. Hence, it is necessary to develop a trust worthy categorization model to classify the patients with or without AD. One of principal modalities commonly used for AD classification is the magnetic resonance imaging (MRI), the analysis of which achieved improved classification performance in categorizing the stages of AD. Hence, this review paper presents the detailed survey of 55 research articles showing different strategies to classify AD. The two important categories of the research papers are the methods using machine learning strategies, and the methods using deep learning strategies. The papers are classified based on various modalities, datasets, performance indices, and the software tools used for implementation. In addition to this, various research gaps and the challenges associated with the existing works of AD classification are discussed. In addition, this review provides the future scope for researchers with the analysis of research issues seen in the literary works. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alzheimer’s disease Deep learning and machine learning Magnetic resonance imaging Mild cognitive impairment

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Open Radio Access Network RIC Empowered Reconfigurable Intelligent Surface: A Physical-Layer Security Perspective

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2024年 2卷 615-625页

作者： Lien, Shao-Yu Tseng, Chih-Cheng Hung, Wei-Cheng Tsai, Cheng-You Liu, Ting-Yu Deng, Der-Jiunn Lin, Yuan-Chun Lin, Shih-Cheng Chang, Chia-Chan Chang, Sheng-Fuh Institute of Intelligent Systems National Yang Ming Chiao Tung University Tainan711010 Taiwan Department of Electrical Engineering National Ilan University Ilan260007 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi621301 Taiwan Department of Computer Science and Information Engineering National Changhua University of Education Changhua50074 Taiwan Department of Electrical Engineering National Chung Cheng University Chiayi621301 Taiwan

The reconfigurable intelligent surface (RIS) steering reflective beam directions toward a target mobile user equipment (UE) has been a promising technology for coverage enhancement and physical-layer (PHY) security to avoid signal eavesdroppers and jammers. However, tracking and predicting the moving trajectory of UE demand significant computing burdens unaffordable to the RIS. Performing beam steering computations on the radio access network (RAN) intelligent controller in the open RAN (O-RAN) architecture therefore emerges as a revolutionary paradigm. In this paper, we propose a PHY-secured beam steering design based on a pre-processing-based Kalman filter algorithm. Through a pre-processing phase to derive the radiation feature, the proposed design accurately tracks the position of the mobile UE and quickly recovers the position search while position tracking fails. Through practical field-try experiments, we justify the capability of O-RAN RIC-empowered RIS for PHY security. © 2013 IEEE.

关键词： Radio access networks

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Secure speech coding using chaotic shift keying for encryption combined with error recovery

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Multimedia Tools and Applications 2025年第7期84卷 3893-3927页

作者： Boumaraf, Messaouda Merazka, Fatiha LISIC Laboratory Telecommunications Department Electronic and Computer Engineering Faculty USTHB University Algiers Algeria LISIC Laboratory Telecommunications Department Electrical Engineering Faculty USTHB University Algiers Algeria

This paper presents a speech encryption algorithm utilizing chaotic shift keying alongside error recovery techniques for the G.722.2 codec. The algorithm employs chaotic data interleaving on speech inter-frames to replace continuous packet loss with isolated losses, thus restoring missing data packets. Codec parameters undergo encryption through the combination of multiple chaotic maps applied to intra sub-frames of speech, facilitating confusion and diffusion processes. Enhanced security is achieved through the utilization of multiple chaotic maps, including chaotic shift keying generators applied to codec sub-frame parameters. Specifically, the chaotic shift keying mechanism is employed using four chaotic maps: a Henon map for xn and yn, a logistic map for zn, a tent map for wn, and a cubic map for un. The keys derived from these maps are utilized for chaotic data interleaving on both inter-frames and intra sub-frames of the speech file to shuffle frame parameters (confusion) and scramble sub-frame parameters (diffusion). Additionally, the proposed system enhances speech quality by mitigating degradation caused by packet loss through a waveform substitution-based recovery method, which replaces lost packets with previously received copies. A series of experiments, including waveform representation, spectrogram analysis, Enhanced Modified Bark Spectral Distortion (EMBSD) test, WideBand Perceptual Evaluation of Speech Quality (WB-PESQ) test, MUltiple Stimuli with Hidden Reference and Anchor (MUSHRA) score, Mean Opinion Score (MOS) score, correlation test, entropy test, Number of Samples Change Rate (NSCR) test, Unified Average Modified Intensity (UACI) test, key space analysis, and sensitivity test, are conducted to evaluate the security and speech quality of the proposed system. The results validate the system’s security and confirm its ability to maintain good audio quality even under packet loss rates ranging from 5% to 20%. © The Author(s), under exclusive licen

关键词： Cryptography

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HORIZON-FREE REGRET FOR LINEAR MARKOV DECISION PROCESSES 12

HORIZON-FREE REGRET FOR LINEAR MARKOV DECISION PROCESSES

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhang, Zihan Lee, Jason D. Chen, Yuxin Du, Simon S. Department of Electrical and Computer Engineering Princeton University United States Department of Statistics and Data Science University of Pennsylvania United States Paul G. Allen School of Computer Science and Engineering University of Washington United States

A recent line of works showed regret bounds in reinforcement learning (RL) can be (nearly) independent of planning horizon, a.k.a. the horizon-free bounds. However, these regret bounds only apply to settings where a polynomial dependency on the size of transition model is allowed, such as tabular Markov Decision Process (MDP) and linear mixture MDP. We give the first horizon-free bound for the popular linear MDP setting where the size of the transition model can be exponentially large or even uncountable. In contrast to prior works which explicitly estimate the transition model and compute the inhomogeneous value functions at different time steps, we directly estimate the value functions and confidence sets. We obtain the horizon-free bound by: (1) maintaining multiple weighted least square estimators for the value functions;and (2) a structural lemma which shows the maximal total variation of the inhomogeneous value functions is bounded by a polynomial factor of the feature dimension. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Markov processes

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Survey of Resources Allocation Techniques with a Quality of Service (QoS) Aware in a Fog Computing Environment

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computers, Materials & Continua 2023年第7期76卷 1291-1308页

作者： Wan Norsyafizan WMuhamad Kaharudin Dimyati Muhammad Awais Javed Suzi Seroja Sarnin Divine Senanu Ametefe School of Electrical Engineering College of EngineeringUniversity Teknologi Mara40450Shah AlamSelangorMalaysia Department of Electrical Engineering Faculty of EngineeringUniversity Malaya50603KualaLumpurMalaysia Department of Electrical and Computer Engineering COMSATS University Islamabad45550Pakistan

The tremendous advancement in distributed computing and Internet of Things(IoT)applications has resulted in the adoption of fog computing as today’s widely used framework complementing cloud ***,suitable and effective applications could be performed to satisfy the applications’latency *** allocation techniques are essential aspects of fog networks which prevent unbalanced load *** resource management techniques can improve the quality of service *** to the limited and heterogeneous resources available within the fog infrastructure,the fog layer’s resources need to be optimised to efficiently manage and distribute them to different applications within the IoT *** has been limited research on resource management strategies in fog networks in recent years,and a limited systematic review has been done to compile these *** article focuses on current developments in resource allocation strategies for fog-IoT networks.A systematic review of resource allocation techniques with the key objective of enhancing QoS is *** involved in conducting this systematic literature review include developing research goals,accessing studies,categorizing and critically analysing the *** resource management approaches engaged in this article are load balancing and task offloading *** the load balancing approach,a brief survey of recent work done according to their sub-categories,including stochastic,probabilistic/statistic,graph theory and hybrid techniques is provided whereas for task offloading,the survey is performed according to the destination of task *** load balancing and task-offloading approaches contribute significantly to resource management,and tremendous effort has been put into this critical ***,this survey presents an overview of these extents and a comparative ***,the study discusses ongoing research issues and potential future directio

关键词： Resource management task offloading load balancing QoS latency energy consumption

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PBb-LMFO: a levy flight integrated MFO inspired ensemble model for cancer diagnosis

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1397-1406页

作者： Behera, Sabita Rani Pati, Bibudhendu Parida, Sasmita Department of Computer Science Rama Devi Women’s University Bhubaneswar India Department of Computer Science and Engineering Institute of Technical Education and Research SoA Univesity Bhubaneswar India

To build a Cancer prediction model based on ML, one needs data of a certain sort, such as gene expression data or microarray data. To reduce the dataset's dimensionality, feature selection is proposed as an optimal solution to high dimensionality challenges and to deal with microarray data, this research work aims to perform the 2-stage feature selection. In the initial stage, the Particle Swarm Optimization (PSO) and Bare-bone PSO (BBPSO) are applied to the dataset separately. Then the common features selected by PSO and BBPSO are considered. Then Levy Flight Moth Flame Optimization (LFMFO) is applied to choose the final optimal set of features. Basic existing ML classifiers are used for the first prediction. Afterwards, the Majority Voting technique is applied to develop the ensemble technique. The proposed model is developed over four Cancer microarray datasets, including CNS, Lung Cancer, Ovarian Cancer, and Breast Cancer. The experimental analysis presents the proposed model obtains the highest accuracy of 98.81% for the Ovarian Cancer dataset. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Bare-bone PSO (BBPSO) Cancer Levy Flight Moth Flame Optimization (LFMFO) Majority voting PSO

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