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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Tiwari, Anurag Indian Institute of Technology School of Artificial Intelligence & Data Science Jodhpur India Babu Banarasi das Institute of Technology& Management Department of Computer Science & Engineering Lucknow India

ISBN: (纸本)9798350364729

Network security is crucial to ensure the protection of information systems in an increasingly connected world, and network traffic attack detection is an important aspect of maintaining system integrity. This study addresses this by investigating the use of a powerful and flexible machine learning algorithm, namely Support Vector Machines (SVM), to detect network traffic attacks. As a result, SVM has been successfully applied in different domains like network security, etc. In this study, SVM is applied for normal and malicious network traffic classification, including several attack types like Denial-of-Service (DoS), User-to-Root (U2R), and Remote-to-Local (R2L) attacks. To this end, the KDD Cup 99 dataset is employed for training and testing the model. These metrics are then used to evaluate the performance of the SVM-based intrusion detection system. The results demonstrate that SVM is able to recognize a high level of accurateness on network data traffic attacks as well as it has performed well in a difficult attack trends. In this case, the F1-scores for the Linear and RBF did result in 96.65/99.24, respectively, indicating that SVM also proved to be an effective algorithm choice in this example. Finally, the study either propose new algorithms or improve existing algorithms for SVM and demonstrate their effectiveness in the area of network security. In conclusion, this study reinforces the importance of SVM as an effective instrument in the detection of attacks on network traffic and strengthening cyber defense capabilities. © 2024 IEEE.

关键词： Network intrusion

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Multi-Head Self-Attention Based Arabic News Recommendation System 6

Multi-Head Self-Attention Based Arabic News Recommendation S...

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6th International Conference on Advanced Aspects of Software engineering, ICAASE 2024

作者： Sabrina, Karboua Harrag, Fouzi Meziane, Farid Departement of Computer Science Mechatronics Laboratory Ferhat ABBAS University Setif 1 Setif Algeria Data Science Research Centre School of Computing and Engineering University of Derby United Kingdom

ISBN: (纸本)9798331522810

This paper introduces an Arabic News Recommendation System (ANRS) designed to enhance personalized news recommendations for Arabic-speaking users. By leveraging a multi-head self-Attention mechanism, the proposed model effectively captures complex dependencies within Arabic text, addressing challenges posed by the language's rich morphology and contextual nuances. The news encoder uses multi-head self-Attention to learn the meaning of Arabic news articles by analyzing interactions between words in titles, body text. Meanwhile, the user encoder models user preferences by examining previously read articles, also using multi-head self-Attention to capture connections between articles. Trained on a dataset of Arabic news articles and 15 million user interaction logs, ANRS achieves a high prediction accuracy of 98.6%, significantly outperforming traditional and deep learning-based Arabic recommendation models. © 2024 IEEE.

关键词： Recommender systems

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Towards Artist Recognition Based on Material Rendering. A Case Study for Recognition of Rembrandt and Van Dyck

Towards Artist Recognition Based on Material Rendering. A Ca...

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IS and T International Symposium on Electronic Imaging 2024: computer Vision and Image Analysis of Art 2024

作者： Huang, Jing Elgammal, Ahmed Department of Big Data and Software Engineering Chongqing University Chongqing China Department of Computer Science Rutgers University NJ United States

Can artists be recognized from the way they render certain materials, such as fabric, skin, or hair? In this paper, we study this problem with a focus on recognizing works by Rembrandt, Van Dyck, and other Dutch and Flemish artists from the same era. This paper proposes a novel material-based approach based on Swin Transformer and Cascade Mask R-CNN to address artist recognition task. We report the performance on a dataset of 644 images. Additionally, the model’s robustness to image variations is studied. © 2024, Society for Imaging science and Technology.

关键词： Rendering (computer graphics)

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Enhancing the Coexistence of 802.11 WLAN and 5G Networks using a Multi-Task Learning with Convolutional Neural Network Model 2

Enhancing the Coexistence of 802.11 WLAN and 5G Networks usi...

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2nd International Symposium on Information Technology and Digital Innovation, ISITDI 2024

作者： Putra, Dody Ichwana Ekariani, Shelvi Universitas Andalas Dept. of Computer Engineering Padang Indonesia Universitas Andalas Dept. of Mathematics and Data Science Padang Indonesia

ISBN: (纸本)9798350389241

The increasing need for high-speed internet for wireless devices has driven the development of 5G New Radio (NR) technology. The 5G NR operates in various spectrums such as sub-1 GHz, sub-6 GHz, and millimeter wave (mmWave) in the 26-28 GHz range. Conversely, the new standard Wi-Fi 6E operates on the unlicensed band and currently utilizes 2.4 GHz, 5 GHz, and 6 GHz. The coexistence of 5G NR sub-6 GHz and Wi-Fi 6E networks can result in interference and reduced performance for both technologies. To address this challenge, we introduce a Multi-Task Learning (MTL) approach using Convolutional Neural Networks (CNN) to improve the coexistence of 5G and Wi-Fi 6E networks. This proposes identifying and classifying packets transmitted and its modulation over both types of networks. The MTL-CNN model is trained to classify packets based on their type (e.g., 5G or Wi-Fi) and to identify the specific protocol used within each packet. We confirm the accuracy of this method with Wi-Fi and 5G signals produced by the MATLAB waveform simulator and subsequently deploy it on a hardware testbed using SDR. Our experimental results show that this method improves the accuracy of packet identification and classification, thus enhancing the coexistence between 5G and Wi-Fi 6E networks. MTL-CNN potentially leads to more efficient use of the wireless spectrum and improves the overall performance of wireless communication systems. © 2024 IEEE.

关键词： Wi-Fi

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Enhancing Hospital Mortality Prediction for Heart Failure Patients through Hyperparameter Optimization and Interpretability Analysis with LIME and SHAP 1

Enhancing Hospital Mortality Prediction for Heart Failure Pa...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Meena, S. Murthy, Monisha Kodipalli, Ashwini Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798331518943

Heart failure is one of the primary causes for deaths caused in the hospital. Predicting mortality rate of such patients is extremely important for the efficient use of health care resources. This research aims to estimate the hospital mortality rate of Heart Failure(HF) patients using machine learning(ML). Using computational technique, the obtained model has given the most efficient accuracy. This paper uses the classification model to detect two outcomes (0 and 1, i.e., if the patient will survive or not). The classification models used for the hospital mortality prediction are, single classifiers such as Logistic regression, SVM, KNN, Decision tree (Ginni and Entropy);with Logistic regression obtaining highest accuracy of 88.9%, Bagging and boosting models such as Randomforest, Adaptive boosting, Gradient boosting, XG boosting, Categorical boosting;with CatBoost obtaining highest accuracy of 90.2%. To achieve the best accuracy, ensemble models were used out of which ensemble classifier ensemble gave 91.1% accuracy. To understand the features playing important role in decision making, explainable AI(XAI) is used. LIME and SHAP are considered. LIME generated local explanations, it explained the decision of a complex model for a specific instance or observation. In contrast, SHAP provided global explanations, it explained the overall behavior of the model across all instances. © 2024 IEEE.

关键词： Adaptive boosting

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Attention-based Multimodal Fusion with Adversarial Network for In-Hospital Mortality Prediction

Attention-based Multimodal Fusion with Adversarial Network f...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： An, Ying Qiu, Ruping Guo, Lin Chen, Xianlai Central South University Big Data Institute Changsha China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

It is crucial to predict the in-hospital mortality for improving clinical decision-making and optimizing hospital resource allocation. Many recent studies have attempted to integrate multimodal data, such as digital time series and clinical notes from electronic health records (EHRs), to improve the performance of mortality prediction. Although current methods show good performance, it is often difficult to effectively capture fine-grained inter-modal correspondences. In addition, the different distributions and heterogeneous properties of the various modalities lead to modality gaps that severely affect the effectiveness of modal fusion. To overcome these limitations, we propose an Attention-based Multimodal Fusion with Adversarial network (AMFA) for in-hospital mortality prediction. In AMFA, feature extraction is first performed to obtain modality-specific features, and then an attention mechanism is employed to capture inter-modal interrelationships. The main difference with existing methods is that AMFA achieves this by calculating the relative importance of individual features focusing attention on the most relevant features and eliminating irrelevant features, and then reassigning attention between relevant features to obtain finer semantic relevance. Subsequently, we introduce a discriminator network that addresses the modality gap by adjusting the distribution of various modal representations through adversarial training. We evaluate AMFA on two publicly available datasets, and experimental results show that AMFA outperforms several state-of-the-art models in the task of in-hospital mortality prediction. © 2024 IEEE.

关键词： Electronic health record

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Defending Against IoT Threats: A Comprehensive Framework with Advanced Models and Real-Time Threat Intelligence for DDoS Detection 2

Defending Against IoT Threats: A Comprehensive Framework wit...

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

作者： Jyothsna, B. Jyothsna, V. Mohan Babu University Department of Computer Science and Engineering Sree Sainath Nagar Tirupati India Mohan Babu University Department of Data Science Sree Sainath Nagar Tirupati India

ISBN: (纸本)9798350365269

The incorporation of Software Defined Networking (SDN) with IoT emerges as an auspicious strategy for enhancing security and access control mechanisms. Nevertheless, substantial threats from DDoS attacks persist in IoT networks, with the potential for execution over botnet or zombie attack, incorporating a machine learning-based detection scheme enables establishing it a framework have been introduced to analyze the performance of IoT devices. Profiles that inform decision-making processes are amassed by these frameworks, ultimately protecting the security of the IoT devices. This work presents a machine learning-based approach for detecting DDoS attacks in an SDN-WISE IoT controller. The incorporation of a machine learning-based detection scheme enables the establishment of a testbed environment for simulating the traffic of DDoS attacks. A logging mechanism within the SDN-WISE controller detects the traffic, generating network logs that are pre-processed and transformed into a dataset. The machine learning DDoS detection module entrenched in the SDN-WISE controller uses Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM) algorithms to categorize SDN-IoT data. The proposed framework's performance is assessed across various traffic simulation scenarios, and the results from the machine learning DDoS detection module are analyzed. The proposed framework exhibits an accuracy rate of 98.2%, 97.1%, and 97.18% for NB, SVM, and DT, respectively. The attack detection module consumes up to 36% of memory and CPU usage, saving approximately 64% of memory while maintaining up to 71% CPU availability to analyze SD-IoT network traffic. The proposed framework achieves an accuracy of 98.2% which proves the efficiency of the work over state of the work models. © 2024 IEEE.

关键词： Support vector machines

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Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

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computers, Materials & Continua 2022年第4期71卷 1095-1109页

作者： R.Joshua Samuel Raj M.Varalatchoumy V.L.Helen Josephine A.Jegatheesan Seifedine Kadry Maytham N.Meqdad Yunyoung Nam Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India Department of Computer Science&Engineering Cambridge Institute of TechnologyBengaluru560036India Department of Computer Applications CMR Institute of TechnologyBengaluru560037India Department of Computer Science&Engineering Swarnandhra College of Engineering and TechnologyNarasapur534280India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Department of Computer Science and Engineering Soonchunhyang University31538Korea

Internet of Things (IoT) is transforming the technical setting ofconventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to theIoT enabled models are resource-limited and necessitate crisp responses, lowlatencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentionedchallenges. But the intrinsic high latency of CC makes it nonviable. The longerlatency degrades the outcome of IoT based smart systems. CC is an emergentdispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes theenergy utilization of the cloud infrastructure and rises the income of serviceproviders by the minimization of the processing time of the user job. Withthis motivation, this paper presents an intelligent Chaotic Artificial ImmuneOptimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabledcloud environment. The proposed CAIOA-RS algorithm solves the issue ofresource allocation in the IoT enabled cloud environment. It also satisfiesthe makespan by carrying out the optimum task scheduling process with thedistinct strategies of incoming tasks. The design of CAIOA-RS techniqueincorporates the concept of chaotic maps into the conventional AIOA toenhance its performance. A series of experiments were carried out on theCloudSim platform. The simulation results demonstrate that the CAIOA-RStechnique indicates that the proposed model outperforms the original version,as well as other heuristics and metaheuristics.

关键词： Internet of things cloud computing task scheduling metaheuristics resource allocation

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Enhancing Agricultural Precision: A Hybrid CNN-SVM Approach for Multidimensional Weed Disease Classification 5

Enhancing Agricultural Precision: A Hybrid CNN-SVM Approach ...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Upadhyay, Deepak Manwal, Manika Deep, Pragya Kukreja, Vinay Sharma, Rishabh Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun248002 India Graphic Era Deemed to Be University Computer Science and Engineering Uttrakhand Dehradun India Iimt College of Engineering Artificial Intelligence & Data Science Uttar Pradesh Greater Noida India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350383522

A new category of weed disease classification in precision agriculture based on a hybrid model using convolutional neural networks (CNN) and support vector machine (SVM) has been presented in the proposed study. In line with overcoming the shortcomings of conventional disease detection approaches, this study introduces a machine learning solution that improves accuracy and productivity in the identification of weed diseases. The hybrid model utilizes the strengths of CNNs for feature extraction and its power with SVM in classification, yielding a stable system that enables multilevel classification of weed disease severity. Creation of a complete collection of weed disease image datasets for the training, validation, and testing phase. The findings from the study show that the use of the model outperforms standalone CNN & SVM models as it achieves a substantial increase in 96.21% accuracy across all the severity levels. The model also uses Grad-CAM visualizations to ensure interpretability and enable a better understanding of the decision-making process. The results imply that the proposed hybrid CNN-SVM model is both efficient in terms of weed disease classification and feasible for use across a range of agricultural settings. This research provides a valuable tool for the development of precision agriculture, which enables early and precise intervention in plant diseases that positively impact sustainable farming. Future work may include increasing the application potential of the model and investigating its integration into automated agricultural systems. © 2024 IEEE.

关键词： Deep Learning Performance Analysis Support Vector Machine Weed Diseases

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OOCDB:A Comprehensive,Systematic,and Real-time Organs-on-a-chip database

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Genomics, Proteomics & Bioinformatics 2023年第2期21卷 243-258页

作者： Jian Li Weicheng Liang Zaozao Chen Xingyu Li Pan Gu Anna Liu Pin Chen Qiwei Li Xueyin Mei Jing Yang Jun Liu Lincao Jiang Zhongze Gu School of Life Science and Technology Southeast UniversityNanjing 210096China School of Biological Science&Medical Engineering Southeast UniversityNanjing 210096China School of Computer Data&Information SciencesUniversity of Wisconsin-MadisonMadisonWI 53706USA

Organs-on-a-chip is a microfluidic microphysiological system that uses microfluidic technology to analyze the structure and function of living human cells at the tissue and organ levels in ***-on-a-chip technology,as opposed to traditional two-dimensional cell culture and animal models,can more closely simulate pathologic and toxicologic interactions between different organs or tissues and reflect the collaborative response of multiple organs to *** the fact that many organs-on-a-chip-related data have been published,none of the current databases have all of the following functions:searching,downloading,as well as analyzing data and results from the literature on ***,we created an organs-on-a-chip database(OOCDB)as a platform to integrate information about organs-on-a-chip from various sources,including literature,patents,raw data from microarray and transcriptome sequencing,several open-access datasets of organs-on-a-chip and organoids,and data generated in our *** contains dozens of sub-databases and analysis tools,and each sub-database contains various data associated with organs-on-a-chip,with the goal of providing researchers with a comprehensive,systematic,and convenient search ***,it offers a variety of other functions,such as mathematical modeling,three-dimensional modeling,and citation mapping,to meet the needs of organs-on-a-chip.

关键词： Organ Mathematical model Citation map Organs-on-a-chip

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