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Electronics and Renewable Systems (ICEARS), International Conference on

作者： S. Pradeep Kumar Suganiya Murugan B. Rubini Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

The recognition of covid-19 is major confront in today’s world, specified as sudden increase in spreading of the disease. Hence, identifying this infection in earlier phase facilitates medicinal fields such as doctors, nurses and lab reporters. This article introduces a novel deep learning technique especially Convolutional Neural Network (CNN) by analyzing features in chest input images. Moreover, this proposed Convolutional Neural Network detects the covid-19 disease under several layers and finally performs binary classification that categorizes input images into covid 19 and non-covid patients. Finally, comparisons had made among all models to predict which model diagnose the disease accurately. To evaluate the overall model performance in detection and classification of covid disease, metrics criterias precision, recall and F1-score are evaluated. Validation analysis were completed for quantify the outcomes via performance measures for each model. This proposed comparison attains maximum accuracy of 100% along with least loss as 0.04 that might diminish human inaccuracy in identification procedure.

关键词： COVID-19 Deep learning Training Renewable energy sources Neural networks Predictive models Convolutional neural networks

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An Efficient Brain tumor classification using CNN and transfer learning

An Efficient Brain tumor classification using CNN and transf...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： P. Sasikumar Srikanth Cherukuvada P Balmurugan P Vijay Anand S. Brindasri R. Nareshkumar Department of Artificial Intelligence and Machine Learning Sphoorthy Engineering College Nadergul Hyderabad Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Computer Science Hindustan Institute of Technology and Science Padur Chennai India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The proper detection of brain tumors as quickly as possible and the administration of efficient therapy are both necessary steps in the process of curing patients. The complexities of tumor morphology, such as size, location, and texture, as well as heteromorphic appearance in medical pictures, make tumor analysis a difficult *** three most frequent types of brain cancer are brain tumors, meningiomas, and pituitary gland tumors. The purpose of this study is to investigate a classification issue known as a triple-class problem in order to correctly identify these *** a pre-trained version of the vgg16 network, the proposed classification method pulls characteristics from brain MRI scans. Classifier models that have shown their worth in practice are given to help put the retrieved attributes into meaningful groups. The presented method is carried out on the Python platform, and its efficacy is evaluated with the use of performance *** to the results, the conventional neural network (CNN) technique was outperformed by the Transfer Learning method, which resulted in the model having a higher overall performance. This can be observed by demonstrating that the model had a stronger overall performance.

关键词： Accuracy Magnetic resonance imaging Transfer learning Brain cancer Brain modeling Feature extraction Pituitary gland

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Three-stage data generation algorithm for multiclass network intrusion detection with highly imbalanced dataset

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International Journal of Intelligent Networks 2023年第1期4卷 202-210页

作者： Chui, Kwok Tai Gupta, Brij B. Chaurasia, Priyanka Arya, Varsha Almomani, Ammar Alhalabi, Wadee Department of Electronic Engineering and Computer Science School of Science and Technology Hong Kong Metropolitan University Hong Kong Taichung 413 Taiwan Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan School of Computing Engineering and Intelligent Systems Ulster University United Kingdom Department of Business Administration Asia University Taiwan School of Information Technology Skyline University College SharjahP.O. Box 1797 United Arab Emirates Al- Balqa Applied University Jordan Immersive Virtual Reality Research Group King Abdulaziz University Jeddah21589 Saudi Arabia Symbiosis International University Pune India Lebanese American University Beirut1102 Lebanon Birkbeck University of London United Kingdom Chandigarh University Chandigarh India

The Internet plays a crucial role in our daily routines. Ensuring cybersecurity to Internet users will provide a safe online environment. Automatic network intrusion detection (NID) using machine learning algorithms has recently received increased attention recently. The NID model is prone to bias towards the classes with more training samples due to highly imbalanced datasets across different types of attacks. The challenge in generating additional training data for minority classes is the generation of insufficient data. The study's purpose is to address this challenge, which extends the data generation ability by proposing a three-stage data generation algorithm using the synthetic minority over-sampling technique, a generative adversarial network (GAN), and a variational autoencoder. A convolutional neural network is employed to extract the representative features from the data, which were fed into a support vector machine with a customised kernel function. An ablation study evaluated the effectiveness of the three-stage data generation, feature extraction, and customised kernel. This was followed by a performance comparison between our study and existing studies. The findings revealed that the proposed NID model achieved an accuracy of 91.9%–96.2% in the four benchmark datasets. In addition, it outperformed existing methods such as GAN-based deep neural networks, conditional Wasserstein GAN-based stacked autoencoder, synthesised minority oversampling technique-based random forest, and variational autoencoder-based deep neural network, by 1.51%–28.4%. © 2023 The Authors

关键词： Support vector machines

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Selecting of Pet Adopters using C4.5 Decision Tree Model Algorithm 2

Selecting of Pet Adopters using C4.5 Decision Tree Model Alg...

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2nd International Conference in Information and computing Research, iCORE 2022

作者： Albia, Criza Jean D. Origines, Domingo Davao Del sur State College Davao del Sur Philippines Institute of Computing Engineering Technology Davao Del sur State College Assistant Professor Information Technology Department Davao Del Sur Digos Philippines

ISBN: (纸本)9798350333909

This study aims to select pet adopters based from the model created from the C4.5 decision tree algorithm to address the proper way for adoption of a pet through online examination. This study uses the method of the C4.5 algorithm by finding the highest gain value to create a decision tree model and generate a decision rules that will apply to a website for decision support if the adopter is qualified or not qualified to adopt. A confusion matrix is used to calculate the accuracy of the C4.5 decision tree algorithm. The information in the confusion matrix is needed to calculate the classification model's performance. This can be done by using performance metrics for the C4.5 algorithm that is based on accuracy, sensitivity, and specificity. The total number of datasets is 1800 which consists of 15 attributes, 1500 for training which consists of 100 instances, and 300 for testing the accuracy is consist of 20 instances. The results on testing the accuracy of the C4.5 decision tree algorithm using confusion matrix is 85%, sensitivity is 84.6% and specificity is 100%. Based on the result of testing the accuracy of the algorithm using the 20 dataset that results with the accuracy value of 85%, sensitivity is 84.6% and specificity is 100%. These results prove that generated model using C4.5 decision tree algorithm is efficient in selecting of pet adopters. © 2022 IEEE.

关键词： Decision trees

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A Survey on Usage of Multimedia Databases for Emotion Elicitation: A Quantitative Report on How Content Diversity Can Improve Performance

A Survey on Usage of Multimedia Databases for Emotion Elicit...

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Proceedings of the International Convention MIPRO

作者： M. Horvat P. Jerčić Department of Applied Computing Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Graz University of Technology Institute of Interactive Systems and Data Science Graz Austria

Affective picture databases provide a standardized set of images to elicit controlled and consistent emotional responses in research participants. They are a valuable tool for studying various emotion-related phenomena across several research domains. These domains include emotion perception, emotion regulation, and the neural basis of emotion. However, affective picture databases have diverse schemas, structures, and content, making them difficult to use. Searching and retrieving optimal pictures relevant to affective stimulation may be challenging and time-consuming. In this context, we surveyed domain experts about their practices and experiences working with affective multimedia databases such as IAPS, NAPS, OASIS, GAPED, and others. The survey identified a need for novel data observatory software. This finding motivates the authors’ intention to develop and validate such software platform that relies on AI. Such a platform would describe better, retrieve, and integrate various semi-structured affective multimedia datasets. The results prominently indicate the overwhelming dissatisfaction regarding stimuli content diversity and cultural bias, specifically regarding emotional and semantic context. The main driver of satisfaction from users of existing automated retrieval software is the quality of semantic descriptors available. This points to the direction AI should take in novel data observatory software. This survey follows up on a similar survey conducted ten years ago and explores the differences in researchers’ opinions and experiences during that time. The complete aggregated results are publicly available at https://***/mhorvat/stimdbsurvey.

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Securing Smart Homes Using Hybrid Stacking Ensemble Deep Learning-Enabled Framework 2

Securing Smart Homes Using Hybrid Stacking Ensemble Deep Lea...

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2nd International engineering Conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Moubayed, Abdallah Injadat, MohammadNoor Abdellatif Mohamed, Tamer Mohamed Almatarneh, Sattam Al-Mashagbeh, Malak Aljaidi, Mohammad School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Lebanese American University Electrical and Computer Engineering Department Byblos Lebanon Western University Electrical and Computer Engineering Department LondonON Canada Zarqa University Data Science and Artificial Intelligence Department Zarqa Jordan School of Engineering Applied Science and Technology Canadian University Dubai Dubai United Arab Emirates Zarqa University Cyber Security Department Zarqa Jordan Zarqa University Computer Science Department Zarqa Jordan

ISBN: (纸本)9798350373363

The added deployment of Internet-of-Things (IoT) devices in various environments has led to people increasingly adding this technology to their homes to make them more comfortable and have a more convenient living environment. Despite the promise and the growth potential of the smart home market, one main concern is the security and privacy of smart home devices. This is because smart home devices equipped with IoT technology are prone to various network-based attacks. With smart home devices having limited computation and memory capabilities, many IoT manufacturers often overlook the security aspect by focusing more on the functionality. To address this issue, the concepts of machine learning (ML) and deep learning (DL) have emerged as viable and promising solutions. To that end, this paper proposes a hybrid stacking ensemble DL-enabled framework that combines both classical ML models with sophisticated DL models. The goal is to make use of the simplicity and interpretability of the classical ML models with the superior time-dependent patterns that DL models can detect. The proposed framework's performance is evaluated using multiple datasets representing different smart home devices. Experimental results show that the proposed framework achieved better detection accuracy, precision, recall, and F-score. © 2023 IEEE.

关键词： Intrusion detection

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Bilinear Graph Neural Network-Enhanced Web Services Classification 23

Bilinear Graph Neural Network-Enhanced Web Services Classifi...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhang, Lulu Cao, Buqing Peng, Mi Qing, Yueying Kang, Guosheng Liu, Jianxun Fletcher, Kenneth K. School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China University of Massachusetts Boston Department of Computer Science Boston United States

ISBN: (纸本)9781665494571

With the growing number of Web services, classifying Web services accurately and efficiently has become a challenging problem. Effective service classification is conducive to improving the quality of service discovery and the efficiency of service composition. Existing graph neural network methods represent the target service node by a weighted sum of the features of the neighbor service nodes, and have achieved remarkable results in service classification. However, these methods ignore the impact of possible interactions between neighbor service nodes on the target service node. To address this problem, we propose a Web services classification method based on bilinear graph neural network. Our proposed method can model the bilinear interactions between neighbor service nodes and effectively improve the accuracy of service classification. First, our proposed model uses the Word2Vec model to extract the latent semantic vectors from service description documents. We then construct the service relationship network according to tags and shared annotation relationships of Web services. Next, a bilinear aggregator is used to model the pairwise interactions between neighbor service nodes, highlighting the local common attributes. Finally, the bilinear aggregator is combined with the weighted sum aggregator to construct a bilinear graph neural network, which can learn more comprehensive representations of services. The experimental results on the real dataset from ProgrammableWeb show that the classification accuracy of the proposed method is greatly improved compared with DeepWalk, Node2Vec, GCN, and GAT. © 2021 IEEE.

关键词： Smart cities Annotations Mashups Semantics Quality of service Feature extraction Graph neural networks

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Real-Time Disease Classification in Coffee Plants with Coral Developer Board Mini

Real-Time Disease Classification in Coffee Plants with Coral...

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Agrosystem engineering, technology & Applications (AGRETA), IEEE International Conference on

作者： Mohammad Hamzah Warsi Hermawan Nugroho Haider Abbas AlMurib Tay Fei Siang Sivaraman Eswaran Department of Electrical and Electronic Engineering University of Nottingham Malaysia Semenyih Malaysia Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Sarawak Malaysia Department of Electrical and Computer Engineering Curtin University Malaysia Miri Malaysia

ISBN: (数字)9798350373660

ISBN: (纸本)9798350373677

Coffee significantly contributes to the socioeconomic development of numerous countries. However, coffee production faces several challenges, including the detrimental effects of coffee leaf diseases, which impact crop yield and quality. Traditional methods for diagnosing coffee leaf diseases are time-consuming and prone to human error. This paper presents an automated system for coffee leaf disease classification with edge computing using the Coral Developer Board Mini, featuring Google's Edge TPU. The system leverages advanced convolutional neural network (CNN) models, including MobileNetV2, VGG-16, VGG19, and DenseNet121, to achieve high accuracy in disease identification. The MobileNetV2 model achieved the highest training accuracy of 98.9%. Inference time comparisons revealed that the Coral Developer Board Mini, with an inference time of 10 milliseconds, outperformed a traditional GPU-based workstation, which had an inference time of 40 milliseconds. Results highlight the efficiency and suitability of the Coral Developer Board Mini for real-time, on-device machine learning applications in agriculture.

关键词： Training Accuracy Computational modeling Scalability Real-time systems Workstations Convolutional neural networks Socioeconomics Diseases Edge computing

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Wi-Wheat+:Contact-free wheat moisture sensing with commodity WiFi based on entropy

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Digital Communications and Networks 2023年第3期9卷 698-709页

作者： Weidong Yang Erbo Shen Xuyu Wang Shiwen Mao Yuehong Gong Pengming Hu College of Information Science and Engineering Henan University of TechnologyZhengzhou450001China Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001China College of International Education Kaifeng UniversityKaifengHenan475004China Knight Foundation School of Computing&Information Sciences Florida International UniversityMiamiFL33199USA Department of Electrical and Computer Engineering Auburn UniversityAuburnAL36849-5201USA

In this paper,we propose a contact-free wheat moisture monitoring system,termed Wi-Wheatþ,to address the several limitations of the existing grain moisture detection technologies,such as time-consuming process,expensive equipment,low accuracy,and difficulty in real-time *** proposed system is based on Commodity WiFi and is easy to *** WiFi CSI data,this paper proposes a feature extraction method based on multi-scale and multi-channel *** feasibility and stability of the system are validated through experiments in both Line-Of-Sight(LOS)and Non-Line-Of-Sight(NLOS)scenarios,where ten types of wheat moisture content are tested using multi-class Support Vector Machine(SVM).Compared with the Wi-Wheat system proposed in our prior work,Wi-Wheatþhas higher efficiency,requiring only a simple training process,and can sense more wheat moisture content levels.

关键词： Channel state information(CSI) WiFi Multi-scale entropy Multi-class support vector machine(SVM) Radio frequency(RF)sensing

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Triple Key Security Algorithm Against Single Key Attack on Multiple Rounds

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Computers, Materials & Continua 2022年第9期72卷 6061-6077页

作者： Muhammad Akram Muhammad Waseem Iqbal Syed Ashraf Ali Muhammad Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Department of Computer Science Superior University Lahore54000Pakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science The Institute of Management Sciences(PAK-AIMS)Lahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Computer Science King Abdulaziz UniversityJeddahSaudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia

In cipher algorithms,the encryption and decryption are based on the same *** are some limitations in cipher algorithms,for example in polyalphabetic substitution cipher the key size must be equal to plaintext otherwise it will be repeated and if the key is known then encryption becomes *** paper aims to improve the said limitations by designing of Triple key security algorithm(TKS)in which the key is modified on polyalphabetic substitution cipher to maintain the size of the key and *** plaintext character is substituted by an alternative *** mode of substitution is transformed cyclically which depends on the current position of the modified *** keys are used in the encryption and decryption process on 8 or 16 rounds with the Exclusively-OR(XOR)of the 1st *** study also identifies a single-key attack on multiple rounds block cipher in mobile communications and applied the proposed technique to prevent the *** utilization of the TKS algorithm,the decryption is illustrated,and security is analyzed in detail with mathematical examples.

关键词： Encryption decryption cipher symmetric key cryptography single key attack

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