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检索条件"机构=Computer Engineering Laboratory-Computer Science Department"

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Recurrent Neural Network Based Hybrid Deep Learning Architecture for Enhanced Network Intrusion Detection 2

Recurrent Neural Network Based Hybrid Deep Learning Architec...

引用

2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Rahman, Md. Nahidur Nijhum, S.M. Rashidul Hasan Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong Raozan4349 Bangladesh Green University of Bangladesh Department of Computer Science and Engineering Narayanganj Dhaka1461 Bangladesh

ISBN: (纸本)9798331517984

Growing cybercrime in an era of widespread internet access presents major challenges to network security, especially in the realm of intrusion detection. While deep learning and machine learning have revolutionized network intrusion detection by helping identify between attacks and regular network traffic, it is still difficult to detect a wide variety of attacks with high accuracy. Additionally, this task becomes more challenging due to the computational demands of analyzing largescale data. To address these challenges, we have proposed a novel approach utilizing a Long Short-Term Memory (LSTM) based recurrent neural network, integrated with Convolutional Neural Network (CNN) architecture. The effectiveness of this model is assessed in our research in comparison to other deep learning techniques, such as basic RNN, LSTM, and GRU. Through extensive experimentation, our proposed hybrid CNN-LSTM model demonstrates superior performance, achieving 98.34% accuracy in multi-class classification and 98.7% accuracy in binary classification, with the most optimized publicly accessible CSE-CIC-IDS2018 dataset. Promising outcomes are also seen in important categorization metrics including F1-score, Precision, and Recall. Our results highlight the effectiveness of the integrated hybrid CNN-LSTM infrastructure as a strong classifier for cyberattack identification. © 2024 IEEE.

关键词： Network intrusion

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A Hybrid Approach Integrating User Preferences and POI Popularity for One-Day Travel Itinerary 8

A Hybrid Approach Integrating User Preferences and POI Popul...

引用

8th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2024

作者： Chaab, Homayoun Shekofteh, Maryam Shiraz University Department of Computer Science and Engineering Shiraz Iran Sarvestan Branch Islamic Azad University Department of Computer Engineering Sarvestan Iran

ISBN: (纸本)9798350374674

The Tourist Travel Design Problem (TTDP) poses a significant challenge in crafting itineraries for tourist's keen on exploring multiple Points of Interest (POIs) within a city while adhering to individual travel constraints. This study introduces a novel hybrid evolutionary approach that combines genetic algorithms and simulated annealing to automate the travel planning process. Empirical findings on real-world datasets illustrate the efficacy of this approach in addressing diverse optimization objectives and generating a varied array of travel itineraries for tourists, thereby confirming its scalability across different problem sizes and optimization goals. Furthermore, the research underscores the importance of personalized visit durations achieved through tailored travel recommendation algorithms, with Memtic-0 algorithms proving particularly effective in prioritizing trip popularity and user interest to enhance overall trip quality and satisfaction. Leveraging simulated annealing, the hybrid algorithm demonstrates superior performance compared to alternative methods such as 2-Opt and 3-Opt, thanks to its stochastic nature facilitating escape from local optima. Optimization through parameterization based on city constraints further enhances convergence speed and effectiveness, emphasizing the critical role of personalized trip duration and efficient convergence strategies in developing robust travel recommendation systems that benefit both users and tourism stakeholders. © 2024 IEEE.

关键词： Simulated annealing

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An Internet of Things Based Mobile Application for Remote Monitoring and Management of Aquariums 1

An Internet of Things Based Mobile Application for Remote Mo...

引用

1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Jacob, Pramod Mathew Moni, Jeni Joy, Alen Gopan, Gayathri Abraham, Alwin Arjun, O. Providence College of Engineering Department of Computer Science and Engineering Kerala Chengannur India

ISBN: (纸本)9798350307795

Aquatic lives are very sensitive in nature and is difficult to manage them in an aquarium. A slight change in the water temperature, dissolved oxygen content or feeding scheme may result in the death of those fishes. So, intense care and monitoring procedures are required for managing aquatic lives in an aquarium. As, Internet of Things (IoT) is integrated in every discipline of life, we propose an IoT based mobile application system for the smart monitoring and management of an aquarium. Temperature sensors and ultrasonic sensors are deployed in the aquarium to sense water temperature and water level respectively. These values will be sent to the Android app with the help of NodeMCU. If any of these values exceeds the threshold automated water management is done with the help of mini water pump. We also deployed a module to add fungal medicines to the aquarium water tank in a periodical manner which may enhance the growth of fishes in a healthier manner. Thus, our model can monitor and manage aquariums remotely over internet in an efficient and economical manner © 2024 IEEE.

关键词： Internet of things

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Predicting the Outcomes of La Liga Matches 1

引用

3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Sharma, Vineet Kaur, Jasleen Kaur, Gurjapna Kumar, Sumit Khanduja, Vidhi Department of Computer Science and Engineering The NorthCap University Gurugram India Department of Computer Science Hansraj College University of Delhi New Delhi India

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

La Liga is the world’s most renowned football league. It attracts and motivates so many people to get involved with this wonderful sport and also creates a great amount of craziness among the people to support their favorite football clubs and players. In this work, a model for forecasting La Liga match results has been build using various machine learning techniques. To achieve the finest results, six machine learning algorithms were employed, coupled with a variety of machine learning methodologies, from pre-processing data to optimizing hyper-parameters. We have used the most significant attributes that are important for our work and has removed all the unwanted attributes. By doing this we have achieved very good accuracy. We removed various other features which were not very useful as they would hinder the prediction and applied ensemble learning approach. The experimental results demonstrate that the Random Forest algorithm allowed us to obtain the highest accuracy possible of 99.37%. We further experimented by using K-fold cross -validation which showed 99.62% accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Generative Adversarial Networks for IoT Security: A Convolutional Neural Network Approach 2

Generative Adversarial Networks for IoT Security: A Convolut...

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2nd IEEE Virtual Conference on Communications, VCC 2024

作者： Fernandez, Brandon Noor, Hafiz Yasir Woungang, Isaac Carvalho, Glaucio H.S. Brock University Department of Computer Science St. CatharinesON Canada Toronto Metropolitan University Department of Computer Science TorontoON Canada Brock University Department of Computer Science and Engineering St. CatharinesON Canada

ISBN: (纸本)9798331530099

The integration of Internet of Things (IoT) devices across various sectors has brought significant advancements but has also heightened security vulnerabilities. To address these challenges, it is essential to develop efficient and accurate detection systems for malicious network traffic at the packet level. This paper presents a novel approach utilizing a Convolutional Neural Network (CNN)-based Generative Adversarial Network (CNN-GAN) to create more robust Intrusion Detection Systems (IDSs) tailored for IoT environments. By leveraging a CNN architecture, the proposed model enhances the discriminator capacity to detect anomalies. Our extensive evaluation on a comprehensive IoT dataset reveals that the CNN-GAN achieves good performance when handling real data and synthetic data. This result underscores the CNN-GAN effectiveness in securing IoT networks against current attacks and emergent threats. © 2024 IEEE.

关键词： Generative adversarial networks

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MRI Scans for Deep Learning-Based Chronic Nephropathy Detection: A Comparison of CNN, MobileNet, VGG16, and ResNet-50 Models 5

MRI Scans for Deep Learning-Based Chronic Nephropathy Detect...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Amanatulla, M.D. Swathi, G. Pallavi, M. Bindu, K. Pavana Computer Science & Engineering Department Lakkireddy Balireddy College of Engineering Mylavaram India

ISBN: (纸本)9798350361155

Prolonged Nephropathy Given that detection usually shows no signs in its early phases and has a high morbidity rate, it presents a serious danger to world health. This research paper proposes a machine learning architecture that uses models based on deep learning (CNN, MobileNet, VGG16, ResNet-50) to identify illness using MRI data. 12, 446 images in the dataset obtained through Kaggle are categorized as normal, cyst, stone, and malignancy. The study evaluates the models' performances, and ResNet-50 exhibits the highest accuracy. The VGG16 and ResNet-50 designs for the proposed system are presented, with a focus on their characteristics. Pre-processing, instruction, classification, and collection of data are all included in the implementation. Users may upload their photos to be classified by different algorithms. The results demonstrate the effectiveness of the ResNet-50 model, which had an overall 98% classification accuracy for renal diseases. In conclusion, the application of deep learning specifically, the ResNet-50 model shows promise as a quick and dependable technique for detection, therefore alleviating a major healthcare issue. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Spectral-Spatial Subspace Detection for Improved Change Detection in Hyperspectral Images 3

Spectral-Spatial Subspace Detection for Improved Change Dete...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Afjal, Masud Ibn Mondal, Md. Nazrul Islam Mamun, Md. Al Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350388282

HyperSpectral Images Change Detection (HSI-CD) plays a pivotal role in precise earth observation through remote sensing, leveraging abundant spectral and spatial information. However, prevailing methods, relying on image difference and band selection, encounter challenges in preserving spectral characteristics, neglecting nonlinear relationships among bands, and addressing high data dimensionality, thus limiting their overall efficacy. In this paper, we introduce S2HNet, a hybrid spectral-spatial network crafted to tackle end-to-end HSI-CD challenges. Our proposed S2HNet excels in efficiently extracting significant spectral-spatial information within a reduced feature space, achieved by integrating a principal component analysis (PCA) based feature subspace reduction module into a hybrid 3D-2D convolutional neural network (CNN). Consequently, the generated subspace demonstrates superior change detection performance compared to related baseline and advanced approaches. Thorough experiments were run on two publicly accessible baseline HSICD datasets: China farmland (CH) and USA irrigated farmland (USA), along with one synthesized dataset, Indian Pines (IP). These experiments rigorously validate the effectiveness and efficiency of the proposed methodology. The results demonstrate the superiority of the proposed approach, achieving the highest change detection performances, specifically 97.51% and 96.57% for CH and USA datasets, respectively, and 99.04% for our synthesized IP dataset. © 2024 IEEE.

关键词： Principal component analysis

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AgriAI: Revolutionizing Agriculture with Data science 2

AgriAI: Revolutionizing Agriculture with Data Science

引用

2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Raut, Nitin B. Ashwin, U. Aswin, S. Bala Selva Vishnu, N. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350365269

In this ambitious agricultural project, cutting-edge technologies such as Machine Learning (ML) and Deep Learning (DL) are harnessed to revolutionize farming practices in India. The primary focus lies in automating vital tasks like crop disease detection, crop recommendation, and fertilizer suggestion, which are traditionally laborious and time-consuming for farmers. Leveraging recent advancements in ML algorithms, a comprehensive solution is envisaged. This innovative approach involves the integration of sophisticated models into a user-friendly website, creating a seamless user experience. Through this platform, farmers gain access to automated tools that significantly enhance their crop management strategies. The platform provides a comprehensive solution by combining deep learning-based crop disease detection mechanisms, intelligent crop recommendation systems, and effective fertilizer recommendations derived from machine learning algorithms. This combination of technologies helps farmers maximize their overall crop yields and minimize losses caused by diseases in addition to streamlining farming procedures. The synergy of ML, and DL on a unified web interface stands as a testament to the transformative power of technology in agriculture, marking a pivotal step toward sustainable and efficient farming practices in India. © 2024 IEEE.

关键词： Support vector machines

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Efficiency & Innovation in Recruitment usingAI & Android

Efficiency & Innovation in Recruitment usingAI & Android

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Chalana, Kanika Gautam, Ashwin Ravi Raj, Anand Daivik, J.S. Amrutha, H.R. Dayananda Sagar College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350359688

The goal of screening resumes is to determine the top applicants for a position and to inform users of their resume score and areas for improvement. The literature on existing approaches has been analyzed, and it has been discovered that the traditional systems like manual screening may result in false assumptions and the wasting of human potential, but they lackrobustness Regarding processing, accuracy and efficiency. To acquire accurate results, software must utilized to process natural language and machine learning techniques to match and rate the candidates in real-time by ranking their resumes. The input would be the resumes and output would be a ranked candidate's resumes on the admin side and recommendations for the user side. Instantaneous real-time output results are acquired by employing Object character recognition. In The system that is being suggested the authors used OCR model and android principles along with python scripts. This system has the many benefits including security, interpretability, high accuracy, lightweight model, and quick processing. © 2024 IEEE.

关键词： Adversarial machine learning

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Using speech characteristics from children's story narratives to detect autistic tendencies through deep learning methods

Using speech characteristics from children's story narrative...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Lai, Yen Yu Chen, Arbee L. P. National Tsing Hua University Department of Computer Science Hsinchu Taiwan Asia University Department of Computer Science and Information Engineering Taichung Taiwan

ISBN: (纸本)9798350362480

The number of children diagnosed with Autism Spectrum Disorder (ASD) is continually increasing. However, diagnosing autism is not straightforward;the process is lengthy and complex. Previous research has indicated that children with autism exhibit deficits in using language within social contexts, such as storytelling skills. Additionally, children with autism may display distinct patterns in certain acoustic features compared to typically developing (TD) children. With the advancement of computational models, we aim to employ deep neural network models to rapidly analyze the acoustic features of children's narratives for detecting autism. In this study, we collected narrative data from 12 children with autism and 19 TD children using Module 3 of the standardized tool ADOS-2 (Autism Diagnostic Observation Schedule, Second Edition). We then represented the acoustic features using Mel-Frequency Cepstral Coefficients (MFCCs) and employed computational models for training and classification. Moreover, we identified 10 low-level descriptors (LLDs) in our dataset through t-tests that showed significant differences between ASD and TD children. We combined these 10 LLDs with MFCCs as inputs to the model and achieved an F1 score of 89.4%. Upon further analysis of these 10 LLDs, we found that they indeed represent the speech characteristic differences between ASD and TD children. This further provides interpretability of our model in identifying autistic tendencies based on the speech characteristic differences. © 2024 IEEE.

关键词： Contrastive Learning

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