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Light gradient boosting machine with optimized hyperparameters for identification of malicious access in IoT network

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Digital Communications and Networks 2023年第1期9卷 125-137页

作者： Debasmita Mishra Bighnaraj Naik Janmenjoy Nayak Alireza Souri Pandit Byomakesha Dash S.Vimal Department of Computer Application Veer Surendra Sai University of TechnologyBurlaSambalpur768018OdishaIndia Department of Computer Science Maharaja Sriram Chandra Bhanja Deo(MSCB)UniversityBaripada757003OdishaIndia Department of Software Engineering HaliçUniversity34394IstanbulTurkey Department of Information Technology Aditya Institute of Technology and Management(AITAM)Tekkali532201Andhra PradeshIndia Data Analytics Lab Department of Artificial Intelligence and Data ScienceRamco Institute of TechnologyNorth Venganallur VillageRajapalayam 626117 Virudhunagar DistrictTamilnaduIndia

In this paper,an advanced and optimized Light Gradient Boosting Machine(LGBM)technique is proposed to identify the intrusive activities in the Internet of Things(IoT)*** followings are the major contributions:i)An optimized LGBM model has been developed for the identification of malicious IoT activities in the IoT network;ii)An efficient evolutionary optimization approach has been adopted for finding the optimal set of hyper-parameters of LGBM for the projected ***,a Genetic Algorithm(GA)with k-way tournament selection and uniform crossover operation is used for efficient exploration of hyper-parameter search space;iii)Finally,the performance of the proposed model is evaluated using state-of-the-art ensemble learning and machine learning-based model to achieve overall generalized performance and *** outcomes reveal that the proposed approach is superior to other considered methods and proves to be a robust approach to intrusion detection in an IoT environment.

关键词： IoT security Ensemble method Light gradient boosting machine Machine learning Intrusion detection

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Image Processing and ArcSoft Based Data Acquisition and Extraction System 2nd

Image Processing and ArcSoft Based Data Acquisition and Extr...

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2nd International Conference on Intelligent and Cloud Computing, ICICC 2021

作者： Yanglu Khan, Asif Yadav, Amit Qinchao Shrestha, Digita Department of Computer Science and Engineering Chengdu Neusoft University Chengdu China Department of Computer Application Integral University Lucknow India University of Electronic Science and Technology of China Chengdu China College of Engineering IT and Environment Charles Darwin University CasuarinaNT Australia

ISBN: (纸本)9789811698729

Characteristic data acquisition and extraction are one of the core realizations of artificial intelligence application development, which is completed through the data output by traditional hardware equipment as the basis, coupled with efficient artificial intelligence applications, and then provide accurate and reliable data support for the data analysis stage. Focusing on image filtering and recognition technology, and framework development applying to portable monitoring systems, here is a realization of data acquisition and extraction system. OpenCV graphics library is used to complete the data acquisition of monitoring terminal and a bilateral Gaussian filter algorithm with configurable parameters adapted to the Raspiberry hardware is designed to filter out noise and smooth the image while saving/protecting the edges. The filtering output as the image sample data basis, a rapid development framework (V1.0) with the ArcSoft artificial intelligence engine as the core is realized to filter out redundant data and extract facial feature data on demand (what users care about) with the conversion and storage in proper format, and such extraction tool is suitable for face data processing. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data acquisition

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Alzheimer’s Disease Prediction from MRI image using Convolutional Deep Neural Network

Alzheimer’s Disease Prediction from MRI image using Convolu...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Sanghamitra Panda Rekha Sahu Satya Ranjan Dash Department of Computer Science and Engineering Centurion University of Technology and management Bhubaneswar Odisha India Department of Computer Science and Engineering Silicon University Bhubaneswar Odisha India School of Computer Application KIIT Deemed to be University Bhubaneswar Odisha India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

Alzheimer’s disease is one sort of neurological disorder which affects the daily activities and it is incurable. But, if it can be detected early, then it may be prevented or made to progress slowly. For detection, deep learning has played a great role now a days. In this paper, we have taken 6400 MRI images of both dementia and non-dementia subjects. The dataset is spilt into ${8 0 \%}$ for training a model and ${2 0 \%}$ for validating and accessing the achievement of the trained model. We have recommended a two-dimensional convolutional neural network architecture in order to apply on the MRI dataset to generate a model for classifying and predicting Alzheimer’s disease patients. The model generated using the proposed two-dimensional architecture has given approximately $98 \%$ classification accuracy. Besides, we have presented roc-auc curve, f1-score, recall, support, precision and confusion matrix to show the execution of the model. Again, at training procedure, model loss and accuracy also depicted according to epoch times. The researches already performed on prediction of Alzheimer’s disease has given poor performance of ${9 1 \%}$ classification accuracy in comparison to our model.

关键词： Training Technological innovation Accuracy Magnetic resonance imaging Computational modeling computer architecture Predictive models Convolutional neural networks Alzheimer's disease Testing

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FedHiDiC: High-Dimensional Heterogeneous Data Condensation and Distillation Contrastive Learning in Federated Learning

FedHiDiC: High-Dimensional Heterogeneous Data Condensation a...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Jiang, Ming Li, Yun Zhang, Feng Guo, Biao Qin, Ping Zhang, Shuo Lu, Yao School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China Metaverse Application Engineering Center Guangxi Nanning530000 China Guangxi Institute of Digital Technology Guangxi Nanning530000 China School of Information Engineering Guangxi Polytechnic of Construction Nanning530000 China

ISBN: (纸本)9781665410205

In response to key challenges in federated learning, particularly those concerning the heterogeneity of locally distributed data which engenders client drift and the problematic occurrence of dimensionality collapse, we propose FedHiDiC: high-dimensional heterogeneous data condensation and distillation contrastive learning. A key idea of FedHiDiC is to integrate mean squared error, standard deviation, and covariance as regularization terms in local training to control parameter norm growth and bolster the capacity to handle high-dimensional, heterogeneous data. Another key idea is to utilize the similarity of representations between models to correct for local training, which reinforces intra-class compactness and inter-class distinction and reduces model variability through knowledge distillation from the global to local models, combined with contrastive strategies. Our experiments on CIFAR10 and CIFAR100 datasets show that FedHiDiC achieves significant performance improvements, surpassing FedAvg, FedProx, and MOON by 2.9% to 4.0% in accuracy, which confirms the effectiveness of FedHiDiC in tackling heterogeneity challenges. © 2024 IEEE.

关键词： Federated learning

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A Framework for Multiclass Contour Visualization

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IEEE Transactions on Visualization and computer Graphics 2023年第1期29卷 353-362页

作者： Li, Sihang Yu, Jiacheng Li, Mingxuan Liu, Le Zhang, Xiaolong Luke Yuan, Xiaoru Laboratory of Machine Perception Ministry of Education School of AI Peking University China Center for Data Science Academy for Advanced Interdisciplinary Studies Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China School of Computer Science Northwestern Polytechnical University China College of Information Sciences and Technology Pennsylvania State University United States

Multiclass contour visualization is often used to interpret complex data attributes in such fields as weather forecasting, computational fluid dynamics, and artificial intelligence. However, effective and accurate representations of underlying data patterns and correlations can be challenging in multiclass contour visualization, primarily due to the inevitable visual cluttering and occlusions when the number of classes is significant. To address this issue, visualization design must carefully choose design parameters to make visualization more comprehensible. With this goal in mind, we proposed a framework for multiclass contour visualization. The framework has two components: a set of four visualization design parameters, which are developed based on an extensive review of literature on contour visualization, and a declarative domain-specific language (DSL) for creating multiclass contour rendering, which enables a fast exploration of those design parameters. A task-oriented user study was conducted to assess how those design parameters affect users' interpretations of real-world data. The study results offered some suggestions on the value choices of design parameters in multiclass contour visualization. © 2022 IEEE.

关键词： Data visualization

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Classification of Potato Leaf Disease using Deep Learning

Classification of Potato Leaf Disease using Deep Learning

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Advancement in Technology (ICONAT), International Conference for

作者： Neelam Singh Deeksha Sharma Saurav Dev Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Deep learning is one of the subdomains of Artificial Intelligence. With the advantages of automatic learning and feature extraction, in recent years, it has become a hot topic in academic and industrial circles. Meanwhile, this has emerged as one of the hotspots within agricultural plant protection research, including plant disease recognition and pest range assessment. Deep learning in plant disease recognition can avoid the shortcomings of artificial selection of features of the disease spot, making the feature extraction of plant diseases more objective, promoting research efficiency, and increasing technological transformation speed. The goal of this work is to analyze potato leaf diseases using CNN in order to determine the model’s optimal performance using a large dataset (10,000 photos) of potato leaves in the analysis. The most effective model will support farmers in making decisions that will keep potato production from declining. This investigation revealed that the model worked better when forty epochs were applied to the dataset of 10,000 photos. Additionally, three different epochs were applied and achieved 100% accuracy for 30 and 50 as achieved 99.97% and 99.98% respectively. By offering recommendations for how to categorise Potato Leaf Disease with the best result, the study made a substantial contribution to the agricultural industry and farmers. While working with big volumes of data, researchers should be concerned about the system because running code takes longer time. To focus this, a dataset including 2152 photos was gathered from Kaggle, with an additional 7848 images sourced from various platforms such as Google and raw data from Potato Field. To determine which epochs performed the best, a range of epochs were applied and results shows that 40 epochs led to 100% accuracy.

关键词： Deep learning Industries Plant diseases Accuracy Codes Plants (biology) Production Feature extraction Internet Protection

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Machine Learning-Based Cybersecurity Optimization in Internet of Things-Enabled Autonomous Vehicles 1

Machine Learning-Based Cybersecurity Optimization in Interne...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Musa, Usman Ibrahim Musa, Aminu Ibrahim Galadima, Yakubu Ibrahim Kantunsung, Ramadan Osman Gupta, Sakshi Dua, Sakshi Lovely Professional University School of Computer Application Punjab India Esgt Benin University Department of Information Technology Cotonou Benin Al-Qalam University Department of Computer Science Katsina Nigeria University College of Engineering Osmania University Telangana India Amity University Amity Institute of Information Technology Uttar Pradesh Noida India

ISBN: (纸本)9798350348637

The swift proliferation of Internet of Things-Enabled Autonomous Vehicles, or IoT-AVs, caused serious apprehensions about potential security weaknesses. In response, we provide a unique solution that improves cybersecurity in IoT-AVs by applying machine learning (ML) approaches. Three critical areas are the focus of our research: intrusion detection optimization (IDOpt), network reliability and latency optimization (NetOpt), and user authentication optimization (AuthOpt). Through biometric approaches which include fingerprint recognition, we accomplish powerful user authentication with 99% accuracy by using convolutional neural networks (CNNs). For Network Reliability and Latency Optimization, the Random Forest Regressor algorithm minimizes downtime and latency issues, improving vehicle performance with 97% accuracy. Additionally, we use the K-Nearest Neighbors (KNN) technique in intrusion detection optimization to protect IoT-AVs against harmful assaults. We use this ML model and obtain a 99% accuracy rate, allowing us to quickly identify and stop intrusions. The entire security posture of IoT-AVs is strengthened greatly by this optimization. To validate the effectiveness of our proposed approach, we compare our results with existing models and algorithms. The outcomes demonstrate the superiority of our ML-based optimizations over the previous approaches. Our integrated methodology establishes new benchmarks for security enhancement in IoT-AVs. © 2024 IEEE.

关键词： Adversarial machine learning

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An Analysis Conducted Retrospectively on the Use: Artificial Intelligence in the Detection of Uterine Fibroid

An Analysis Conducted Retrospectively on the Use: Artificial...

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International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions)

作者： Ruchi Kaushik Kanta Prasad Sharma Sidharth Malik Manjula Shanbhog Department of Computer Science Christ(Deemed to be Univeristy) Delhi NCR Campus Ghaziabad India Department of Computer Engineering and Application GLA University Mathura India

ISBN: (数字)9798350350357

ISBN: (纸本)9798350350364

The most frequent benign pelvic tumors in women of age of conception are uterine fibroids, sometimes referred to as leiomyomas. Ultrasonography is presently the first imaging modality utilized as clinical identification of uterine fibroids since it has a high degree of specificity and sensitivity and is less expensive and more widely accessible than CT and MRI examination. However, certain issues with ultrasound based uterine fibroid diagnosis persist. The main problem is the misunderstanding of pelvic and adnexal masses, as well as subplasmic and large fibroids. The specificity of fibroid detection is impacted by the existing absence of standardized image capture views and the variations in performance amongst various ultrasound machines. Furthermore, the proficiency and expertise of ultra sonographers determines the accuracy of the ultrasound diagnosis of uterine fibroids. In this work, we created a Deep convolutional neural networks (DCNN) model that automatically identifies fibroids in the uterus in ultrasound pictures, distinguishes between their presence and absence, and has been internally as well as externally validated in order to increase the reliability of the ultrasound examinations for uterine fibroids. Additionally, we investigated whether Deep convolutional neural networks model may help junior ultrasound practitioners perform better diagnostically by comparing it to eight ultrasound practitioners at different levels of experience.

关键词： Ultrasonic imaging Sensitivity Ultrasonography Market research Reliability Convolutional neural networks Artificial intelligence

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The Performance Analysis of Distributed Denial of Service Attacks in Wireless Networks 2

The Performance Analysis of Distributed Denial of Service At...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Agrawal, Trapty Raghavendra, R. Singh, K. Ranjith Rajiv Gandhi, N. Dhabliya, Dharmesh Katariya, Jitendra Kumar Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science and Information Technology Karnataka Bangalore India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Civil Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350308846

Complete studies paper that examines the outcomes that malicious 0.33-celebration actors will have on Wi-Fi networks via dispensed denial of carrier (DDoS) attacks. This paper explores the available numerous assault models and the potential countermeasures that may be applied if you want to mitigate the damage due to such assaults. In addition, this paper focuses on the capability blessings related to deploying preventative measures to lessen the impact of such assaults. Moreover, the paper investigates existing attack trends. It affords an outline of the safety techniques to be had for limiting the harm caused by DDoS attacks in wireless networks. By comparing and analyzing the performance of diverse protection mechanisms, this paper offers an evidenceprimarily based evaluation of the effectiveness of DDoS attack safety answers. ultimately, this paper concludes with the aid of supplying recommended measures for strengthening and ensuring the safety of Wi-Fi networks against malicious actors. © 2023 IEEE.

关键词： Evaluation Measures Performance Recommended Strengthening

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Distributed Split Computing Using Diffusive Metrics for UAV Swarms

arXiv

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arXiv 2025年

作者： Sarı, Talip Tolga Seçinti, Gökhan Trotta, Angelo Department of Computer Science and Engineering University of Bologna Italy Department of Computer Engineering Istanbul Technical University Turkey BTS - Digital Twin Application and Research Center Istanbul Technical University Turkey

In large-scale UAV swarms, dynamically executing machine learning tasks can pose significant challenges due to network volatility and the heterogeneous resource constraints of each UAV. Traditional approaches often rely on centralized orchestration to partition tasks among nodes. However, these methods struggle with communication bottlenecks, latency, and reliability when the swarm grows or the topology shifts rapidly. To overcome these limitations, we propose a fully distributed, diffusive metric-based approach for split computing in UAV swarms. Our solution introduces a new iterative measure, termed the aggregated gigaflops, capturing each node’s own computing capacity along with that of its neighbors without requiring global network knowledge. By forwarding partial inferences intelligently to underutilized nodes, we achieve improved task throughput, lower latency, and enhanced energy efficiency. Further, to handle sudden workload surges and rapidly changing node conditions, we incorporate an early-exit mechanism that can adapt the inference pathway on-the-fly. Extensive simulations demonstrate that our approach significantly outperforms baseline strategies across multiple performance indices, including latency, fairness, and energy consumption. These results highlight the feasibility of large-scale distributed intelligence in UAV swarms and provide a blueprint for deploying robust, scalable ML services in diverse aerial networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Swarm intelligence

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