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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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FedQP: Large-Scale Private and Flexible Federated Query Processing

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IEEE Access 2025年 13卷 92019-92035页

作者： Almohri, Hussain M. J. Watson, Layne T. Kuwait University Department of Computer Science Kuwait City Kuwait Virginia Tech Departments of Computer Science Mathematics Aerospace and Ocean Engineering BlacksburgVA United States

State-of-the-art federated learning methods orchestrate iterations of the stochastic gradient descent algorithm among a network of clients to refine a unified set of model parameters, all while safeguarding individual client datasets. Although promising, the success of existing approaches hinges on using a uniform data model with the same optimization and learning algorithms by the participants. In addition, existing methods can be subject to privacy attacks, such as model-inversion attacks. A pivotal challenge lies in crafting an algorithm that can efficiently aggregate client inputs, ensuring minimal client dataset exposure and maximum flexibility in model adaptation. We propose a new scheme for aggregating local, diverse, and independent data models, addressing data inconsistency and model-inversion attacks. The proposed scheme, federated query processing FedQP, enables clients to build local models independently, without coordinating with the server or other clients. The server communicates with selected clients only to predict future values. Our scheme only requires one communication round between each client and the server for model initialization without the need for a training phase. Clients do not share data or model parameters. We evaluated FedQP by assessing the impact of our new aggregation mechanism on model accuracy and the communication efficacy of our architecture. We show that the caching algorithm in FedQP reduces redundant client communications during query processing. In addition, we present a privacy analysis that shows that our method outperforms prominent gradient-based approaches to federated learning. Our experiments show that FedQP consistently achieves higher classification accuracy in non-IID settings and demonstrates stronger resilience to reconstruction attacks compared to gradient-based methods such as federated averaging (FedAvg). © 2013 IEEE.

关键词： Federated learning

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An Advanced Brain Tumour Detection Framework Using Improved Fast Mask R-CNN Techniques for Enhanced Accuracy

An Advanced Brain Tumour Detection Framework Using Improved ...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Shankar, G. Kumar, V.R. Abhishek Srikanth, Akkaru Reddy, Thummala Jathin Sahil, Shaik Rmd Engineering College Department of Computer Science and Engineering India Rmd Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798331510022

To enhance the precision of diagnosis, this research provides a new structure for identifying brain tumors that integrates an Improved Fast Mask Region based Convolutional Neural Network (IFMRCNN) with complex image processing algorithms. Identification of brain tumors is still a significant difficulty in medical imaging since successful therapy depends on early and accurate identification. By precisely identifying tumor boundaries in brain MRI scans, the proposed model makes use of an enhanced version of FMRCNN is intended to enhance separating and categorizing performance. Sophisticated image processing methods are used to improve the extraction of features, allowing the model to more accurately differentiate between tumor and non-tumor areas. By lowering false positives and true negatives, the structure seeks to maintain computing power while greatly increasing detection rates. The proposed strategy outperforms conventional techniques in terms of reliability, specificity, and sensitivity, according to the results of experiments, which makes it a viable tool for support with clinical choices as well as initial diagnosis in neuroimaging. IFRCNN was applied using the Open CV package in conjunction with an SVM algorithm. Using 218 photos as an initial training set, this study's approach demonstrated a 100% success rate in classifying meningiomas and 87.5% accuracy in classifying gliomas, with an average conviction score of 94.6% in the segments of meningioma tumors. A neurologist does a manual examination and ground truth assessment to verify the divided tumor areas. © 2024 IEEE.

关键词： Convolutional neural networks

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A multimodal sentiment analysis system for recognizing person aggressiveness in pain based on textual and visual information

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Journal of Ambient Intelligence and Humanized Computing 2023年第4期14卷 4489-4501页

作者： Ghosh, Anay Dhara, Bibhas Chandra Pero, Chiara Umer, Saiyed Department of Computer Science and Engineering University of Engineering and Management Newtown Kolkata India Department of Information Technology Jadavpur University Kolkata India Department of Computer Science University of Salerno Fisciano Italy Department of Computer Science and Engineering Aliah University Kolkata India

This article proposes a multimodal sentiment analysis system for recognizing a person’s aggressiveness in pain. The implementation has been divided into five components. The first three steps are related to a text-based sentiment analysis system to perform classification tasks such as predicting the classes into non-aggressive, covertly aggressive, and overtly aggressive classes. The remaining two components are related to an image-based sentiment analysis system. A deep learning-based approach has been employed to do feature learning and predict the three types of pain classes. An aggression dataset for the text-based system and the UNBC-McMaster database for an image-based system has been employed, respectively. Experimental results have been compared with the state-of-the-art methods, showing the superiority of the proposed approach. Finally, the scores due to text-based and image-based sentiment analysis systems are fused to obtain the performance for the proposed multimodal sentiment analysis system. © 2023, The Author(s).

关键词： Sentiment analysis

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IensNet: A novel and efficient approach for iris spoof detection via ensemble of deep models

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Multimedia Tools and Applications 2025年 1-30页

作者： Sharma, Deepika Selwal, Arvind School of Computer Science Engineering and Technology Bennett University Greater Noida201310 India Department of Computer Science and Information Technology Central University of Jammu Samba India

Iris biometrics allow contactless authentication, which makes it widely deployed human recognition mechanisms since the couple of years. Susceptibility of iris identification systems remains a challenging task due to diversity in spoof or presentation attacks (PAs) that fails to assure consistency while adopting them in real life scenarios. Hence, iris PAs are the growing concerns that gained significant attention in recent past decade. To alleviate these attacks or recognize presentation attack instruments (PAIs), iris presentation attacks detection (IPAD) algorithms are designed to distinguish a real and fabricated iris trait. Aiming at the efficient iris spoof detection mechanism, in this research work we expound a novel ensemble learning-enabled model (IensNet) that learns three pre-trained and fined-tuned deep models (i.e. DenseNet161, ResNet and VGGNet) for better accuracy and generalized performance. The novel IensNet approach offers several merits (i.e. consolidated strengths of multiple models, improved generalization ability, etc.) as compared to a simple transfer learning strategy where the knowledge is drawn from single pre-trained model. Finally, our approach learns a novel fully-connected dual layer classifier via outcome of three fine-tuned models to yield a final classification result as bonafide or spoof iris trait. Our approach is evaluated on Notre Dame LivDet iris 2017 and Notre Dame contact lenses 2015 anti-spoofing datasets. The experimental analysis of IensNet offers outstanding performance with a lower ACER of 0.2% and 1.4% for Iris-LivDet-2017 and Notre Dame contact lenses 2015 dataset respectively. Besides, IensNet exhibit promising results in cross-dataset environment with an ACA of 91.46%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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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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Breaking language barriers with ChatGPT: enhancing low-resource machine translation between algerian arabic and MSA

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International Journal of Information Technology (Singapore) 2024年 1-10页

作者： Babaali, Baligh Salem, Mohammed Alharbe, Nawaf R. Department of Computer Science University of Mascara Mascara Mascara 29000 Algeria Department of Computer Science College of Computer Science and Engineering Madinah Madinah Saudi Arabia

Neural machine translation (NMT) has become an essential tool for breaking down language barriers and facilitating communication between different cultures and communities. However, NMT’s potential impact is limited in low-resource language settings, where the availability of training data and resources is scarce. In this context, this article presents an experiment that explores the effectiveness of using a pre-trained language model, ChatGPT, to improve translation performance between the Algerian Arabic dialect and Modern Standard Arabic (MSA). The results of the experiment highlight the potential of using large pre-trained language models to improve translation quality in low-resource language pairs, contributing to the promotion and preservation of minority languages. In addition to the improved translation quality, another noteworthy aspect of the experiment is that ChatGPT was able to capture the intention of the researchers, even when dealing with a low-resource language pair. This means that the model was able to accurately translate the meaning of the sentences, rather than simply producing a word-for-word translation. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Algerian Arabic dialect ChatGPT Low resource languages Modern Standard Arabic Neural machine translation

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Facial Feature Extraction Review for Contemporary applications 15

Facial Feature Extraction Review for Contemporary applicatio...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Singh, Gaganjot Kaur, Shinepreet Devi, Shivali Chandigarh University Department of Computer Science and Engineering Mohali India Department of Computer Science and Engineering India

ISBN: (纸本)9798350370249

Recognizing a face is an intricate cognitive process that showcases the remarkable capabilities of the human brain in visual perception, a phenomenon deeply rooted in evolutionary biology. In an attempt to emulate this complex phenomenon, computer vision technology has been developed to serve as a sophisticated tool capable of recognizing and distinguishing faces, akin to the human visual system, particularly in the domain of biometric identification methods employed for verifying individuals' identities. This is done by identifying the unique features of a human's face through various algorithms. This paper reviews the efficacy of popular approaches to portraying facial features in face pictures. Geometrical-based methods, template-based methods, and appearance-based methods were traditional ways for extracting facial features before the onset of Machine learning for this application. Accuracy through confusion matrix have been comprehended and traditional methods have been found more fit for static and atomic systems although, machine learning algorithms give better and consistent results at the cost of developing and maintaining the computational system underneath. © 2024 IEEE.

关键词： 468 landmark detection discrete wavelet transform eigen faces Facial features detection geodesic distance local binary pattern morphometry

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Multi-Object Detection in 3D Point Cloud's Range Image Using Deep-Learning Technique 14

Multi-Object Detection in 3D Point Cloud's Range Image Using...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Jyothsna, D. Chandra, G. Ramesh Department of Computer Science and Engineering Jntuh Hyderabad India Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350344837

In recent years, LIDARs (Light Detection and Ranging) have gained a lot of insight into various fields such as agriculture, astronomy, robotics, autonomous driving, forestry, etc. Point Clouds obtained through LIDAR have gained attention in the field of computer vision, robotics, and autonomous driving. In computer vision, there are several high level tasks namely classification, detection, tracking, segmentation and registration. Object detection in 3D Point Clouds is one of the challenges faced in the field of computer vision, which is one of the high level tasks. Due to the unordered nature of point cloud data, detecting objects by designing deep neural networks is not a straightforward task. This paper aims to provide a solution to detect the objects in a 3D point cloud data by transforming into 2D range image obtained from LIDAR using the improved YOLOV5 (You Only Look Once- Version5) approach by embedding the coordinate attention module. The improved YOLOV5 is used to detect objects in an image and conducted experiments on different sizes of custom 3D point cloud datasets with and without augmentation. The experimental results showed how the precision, recall and mean average precision (mAP) metrics improve as the dataset size increases. Also, the comparative results with YOLOV5 yielded better results on our data. © 2024 IEEE.

关键词： Object detection

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Hybrid feature selection method for predicting software defect

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Journal of engineering and Applied science 2024年第1期71卷 124页

作者： Anju, A.J. Judith, J.E. Department of Computer Science Engineering Eranad Knowledge City Technical Campus Kerala Manjeri India Department of Computer Science Engineering Noorul Islam Center for Higher Education Tamil Nadu Kanyakumari India

To address the challenges associated with the abundance of features in software datasets, this study proposes a novel hybrid feature selection method that combines quantum particle swarm optimization (QPSO) and principal component analysis (PCA). The objective is to identify a subset of relevant features that can effectively contribute to the accuracy of a predictive model based on an artificial neural network (ANN). The quantum particle swarm optimization algorithm is employed to optimize the selection of features by simulating the behavior of quantum particles in a search space. This approach enhances the exploration and exploitation capabilities, allowing for a more effective identification of relevant features. Furthermore, principal component analysis is integrated into the hybrid method to reduce dimensionality and remove multicollinearity among features, thereby improving the efficiency of the feature selection process. The proposed hybrid method is applied to software defect datasets, where the selected subset of features is fed into an artificial neural network for defect prediction. The performance of the hybrid model is compared with traditional feature selection methods, standalone QPSO, and PCA. Experimental results demonstrate the effectiveness of the hybrid approach in achieving superior predictive accuracy while reducing the dimensionality of the dataset. The proposed approach not only enhances prediction accuracy but also provides a more interpretable and efficient subset of features for building robust defect prediction models. © The Author(s) 2024.

关键词： Feature Selection

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