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International Journal of Mathematical, engineering and Management sciences 2025年第4期10卷 1032-1054页

作者： Purohit, Naresh Panwar, Subhash Department of Computer Science & Engineering Government Engineering College Bikaner Rajasthan Bikaner India

Since author’s writing styles are often ambiguous, writer recognition is an appealing research problem for handwritten manuscript investigation. Pattern identification allows for recognizing the author of a handwritten work. Due to the variety of text visuals, especially handwriting images, author recognition is challenging. Convolution Neural Network (CNN) excels in many fields. This paper presents a complete framework that emphasizes feature engineering and uses a simple, efficient deep neural network. Writer recognition begins with data collection. Programming a technique to generate several database texts enhances the data. To capture and categorize feature information, a multi-path CNN with a Bidirectional Long Short Term Memory (Bi-LSTM) module is developed. By integrating CNN and Bi-LSTMs, the proposed model combines spatial and temporal information, offering a comprehensive representation of handwriting. This synergy makes it particularly effective in handling the variability and complexity of offline writer recognition. Languages are occasionally blended when writing in multilingual regions. The system performed well on two publicly accessible handwritten English language benchmark databases, CVL and IAM, in addition to an in-house bilingual database comprising English and Hindi scripts. This study is unconventional since it addressed offline writer recognition using monolingual and bilingual handwritten text corpora. Analytically, our findings are encouraging compared to previous studies. The model achieves accuracies of 98.78% on the IAM dataset, 98.55% on the CVL dataset, and 99% on an in-house bilingual dataset. These results demonstrate a significant improvement over other state-of-the-art methodologies in offline writer recognition. Copyright © Ram Arti Publishers.

关键词： Feature Selection

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Application of Physical Unclonable Function for Lightweight Authentication in Internet of Things

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computers, Materials & Continua 2023年第4期75卷 1901-1918页

作者： Ahmad O.Aseeri Sajjad Hussain Chauhdary Mohammed Saeed Alkatheiri Mohammed A.Alqarni Yu Zhuang Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science Texas Tech UniversityLubbockUSA

IoT devices rely on authentication mechanisms to render secure message *** data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted by IoT *** application of physical unclonable functions(PUFs)ensures secure data transmission among the internet of things(IoT)devices in a simplified network with an efficient time-stamped *** paper proposes a secure,lightweight,cost-efficient reinforcement machine learning framework(SLCR-MLF)to achieve decentralization and security,thus enabling scalability,data integrity,and optimized processing time in IoT *** has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device *** IoT network gathers information of interest from multiple cluster members selected by the proposed *** addition,the software-defined secured(SDS)technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT *** analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime,energy,secured data retrieval rate,and performance *** enabling the proposed framework,number of residual nodes is reduced to 16%,energy consumption is reduced by up to 50%,almost 30%improvement in data retrieval rate,and network lifetime is improved by up to 1000 msec.

关键词： Cyber-physical systems security data aggregation Internet of Things physical unclonable function swarm intelligences

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IDBNWP: Improved deep belief network for workload prediction: Hybrid optimization for load balancing in cloud system

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Multimedia Tools and Applications 2024年第16期84卷 1-19页

作者： Ajil, A. Kumar, E. Saravana Department of Computer Science and Engineering The Oxford College of Engineering Visvesvaraya Technological University Karnataka Belagavi India School of Computer Science and Engineering REVA University Karnataka Bengaluru India

The achievement of cloud environment is determined by the efficiency of its load balancing with proper allocation of resources. The proactive forecasting of future workload, accompanied by the allocation of resources, has emerged as a primary method for addressing other inbuilt problems, such as the underneath or over utilization of physical machines, resource wastage, VM migration, Quality-of-Services (QoS) violations, load balancing, and so on. In this paper, we have introduced a novel workload prediction and load balancing approach which includes two major phases like workload prediction with deep learning and optimal load balancing. In the initial workload prediction stage, we have proposed an Improved Deep Belief Network (IDBN), which efficiently predict the load as under load, overload or equally balanced. Afterwards, the load gets balanced by the utilization of the hybrid optimization named Bald Eagle Assisted Butterfly Optimization Algorithm (BEABOA), which consider the constraints like makespan (70), communication cost (4000), response time (1.1), turnaround time (2), migration cost (0.1) during the process of optimal load balancing. Also, the outcomes demonstrate that this proposed workload prediction and load balancing approach can offer superior outcomes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Resource allocation

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ANovel Light Weight CNN Framework Integrated with Marine Predator Optimization for the Assessment of Tear Film-Lipid Layer Patterns

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computer Modeling in engineering & sciences 2023年第7期136卷 87-106页

作者： Bejoy Abraham Jesna Mohan Linu Shine Sivakumar Ramachandran Department of Computer Science and Engineering College of Engineering MuttatharaThiruvananthapuramKerala695008India Department of Computer Science and Engineering Mar Baselios College of Engineering and TechnologyThiruvananthapuramKerala695015India Department of Electronics and Communication Engineering College of Engineering TrivandrumKerala695016India

Tear film,the outermost layer of the eye,is a complex and dynamic structure responsible for tear *** tear film lipid layer is a vital component of the tear film that provides a smooth optical surface for the cornea and wetting the ocular *** eye syndrome(DES)is a symptomatic disease caused by reduced tear production,poor tear quality,or excessive *** diagnosis is a difficult task due to its multifactorial *** of several clinical tests available,the evaluation of the interference patterns of the tear film lipid layer forms a potential tool for DES *** instrument known as Tearscope Plus allows the rapid assessment of the lipid layer.A grading scale composed of five categories is used to classify lipid layer *** reported work proposes the design of an automatic system employing light weight convolutional neural networks(CNN)and nature inspired optimization techniques to assess the tear film lipid layer patterns by interpreting the images acquired with the Tearscope *** designed framework achieves promising results compared with the existing state-of-the-art techniques.

关键词： Dry-eye syndrome tearscope plus tear film deep neural networks

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Advancements in training and deployment strategies for AI-based intrusion detection systems in IoT: a systematic literature review

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Discover Internet of Things 2025年第1期5卷 1-33页

作者： Mallidi, S. Kumar Reddy Ramisetty, Rajeswara Rao Computer Science and Engineering Jawaharlal Nehru Technological University AP Kakinada533003 India Computer Science and Engineering Sri Vasavi Engineering College AP Tadepalligudem534101 India Computer Science and Engineering Jawaharlal Nehru Technological University Gurajada AP Vizianagaram535003 India

As the Internet of Things (IoT) grows, ensuring robust security is crucial. Intrusion Detection Systems (IDS) protect IoT networks from various cyber threats. This systematic literature review (SLR) explores the advancements in training and deployment strategies of Artificial Intelligence (AI) based IDS within IoT environments. The study begins by outlining prevalent IoT attacks and developing an updated taxonomy to enhance understanding of these threats. It then examines various IDS architectures and delves into the integration of machine learning (ML) and deep learning (DL) technologies that improve detection capabilities and system responsiveness. Significant emphasis is placed on analyzing different IDS training paradigms-centralized, distributed, and federated learning-and deployment strategies, including in cloud, fog, and edge layers. Their effectiveness within IoT contexts is evaluated comprehensively. Moreover, the review assesses the datasets commonly used for training and validating IDS and discusses key performance metrics and validation measures critical for assessing IDS effectiveness. The study concludes by synthesizing the major challenges facing current IDS implementations in IoT and suggesting future research directions aimed at overcoming these hurdles. This review highlights the technological advancements in IDS and sets the stage for future enhancements in IoT security, emphasizing the integration of innovative training and deployment strategies. © The Author(s) 2025.

关键词： Federated learning

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

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computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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Augmented FCN: rethinking context modeling for semantic segmentation

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science China(Information sciences) 2023年第4期66卷 193-211页

作者： Dong ZHANG Liyan ZHANG Jinhui TANG School of Computer Science and Engineering Nanjing University of Science and Technology College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics

The effectiveness of modeling contextual information has been empirically shown in numerous computer vision tasks. In this paper, we propose a simple yet efficient augmented fully convolutional network(AugFCN) by aggregating content-and position-based object contexts for semantic ***, motivated because each deep feature map is a global, class-wise representation of the input,we first propose an augmented nonlocal interaction(AugNI) to aggregate the global content-based contexts through all feature map interactions. Compared to classical position-wise approaches, AugNI is more efficient. Moreover, to eliminate permutation equivariance and maintain translation equivariance, a learnable,relative position embedding branch is then supportably installed in AugNI to capture the global positionbased contexts. AugFCN is built on a fully convolutional network as the backbone by deploying AugNI before the segmentation head network. Experimental results on two challenging benchmarks verify that AugFCN can achieve a competitive 45.38% mIoU(standard mean intersection over union) and 81.9% mIoU on the ADE20K val set and Cityscapes test set, respectively, with little computational overhead. Additionally, the results of the joint implementation of AugNI and existing context modeling schemes show that AugFCN leads to continuous segmentation improvements in state-of-the-art context modeling. We finally achieve a top performance of 45.43% mIoU on the ADE20K val set and 83.0% mIoU on the Cityscapes test set.

关键词： semantic segmentation context modeling long-range dependencies attention mechanism

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A Novel Approach for Network Vulnerability Analysis in IIoT

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computer Systems science & engineering 2023年第4期45卷 263-277页

作者： K.Sudhakar S.Senthilkumar Department of Computer Science and Engineering University College of EngineeringPattukkottaiRajamadam614701India

Industrial Internet of Things(IIoT)offers efficient communication among business partners and *** an enlargement of IoT tools connected through the internet,the ability of web traffic gets *** to the raise in the size of network traffic,discovery of attacks in IIoT and malicious traffic in the early stages is a very demanding issues.A novel technique called Maximum Posterior Dichotomous Quadratic Discriminant Jaccardized Rocchio Emphasis Boost Classification(MPDQDJREBC)is introduced for accurate attack detection wi th minimum time consumption in *** proposed MPDQDJREBC technique includes feature selection and ***,the network traffic features are collected from the *** applying the Maximum Posterior Dichotomous Quadratic Discriminant analysis to find the significant features for accurate classification and minimize the time *** the significant features selection,classification is performed using the Jaccardized Rocchio Emphasis Boost *** Rocchio Emphasis Boost Classification technique combines the weak learner result into strong *** Rocchio classification technique is considered as the weak learners to identify the normal and ***,proposed MPDQDJREBC technique gives strong classification results through lessening the quadratic *** assists for proposed MPDQDJREBC technique to get better the accuracy for attack detection with reduced time *** assessment is carried out with UNSW_NB15 Dataset using different factors such as accuracy,precision,recall,F-measure and attack detection *** observed results exhibit the MPDQDJREBC technique provides higher accuracy and lesser time consumption than the conventional techniques.

关键词： Industrial internet of things(iiot) attack detection features selection maximum posterior dichotomous quadratic discriminant analysis jaccardized rocchio emphasis boost classification

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A supervised deep learning-based sentiment analysis by the implementation of Word2Vec and GloVe Embedding techniques

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Multimedia Tools and Applications 2025年第2期84卷 979-1012页

作者： Rakshit, Pranati Sarkar, Avik Computer Science & Engineering JIS College of Engineering Block A Phase III West Bengal Kalyani741235 India

Sentiment analysis provides valuable insights into people’s opinions, emotions, and attitudes, enabling businesses to make more informed decisions, improve customer satisfaction, and stay competitive in today’s market. Now-a-days due to the accessibility of social networking platforms like Twitter, Instagram, Facebook, WeChat, etc a bulk of data is being generated within a very small span of time. These data inherit highly potential information hidden in them. Consequently, these data may be analyzed using powerful tools of deep learning techniques to achieve tremendous societal benefits. Keeping these views in mind, the authors have planned an extensive experimental study to undertake the task of sentiment analysis. In this present work, thirty-six different Word2Vec and GloVe Embedded deep learning models have been developed on the deep learning based architecture namely Multilayer Perceptron (MLP), Convolutional Neural Network(CNN), Long-and-Short-Term Memory Network (LSTM), Bi-directional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and Bi-directional GRU (Bi-GRU). All the developed models are compared based on their accuracies, F1-scores, and other evaluation parameters. Several promising results have emerged from this study. It has been observed that the Word2Vec embedded Bi-directional GRU model yields the best result with an average F1-score of 0.84 in the case of a train-test ratio of 80:20. Finally this type of elaborated comparative and comprehensive study may be considered unique in its nature and supposed to be used to develop expert sentiment analyser. © 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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