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Evaluating Quantum machine learning Approaches for Histopathological Cancer Detection: Classical, Hybrid Simulation, and IBM Quantum Computing 4

Evaluating Quantum Machine Learning Approaches for Histopath...

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4th IEEE international conference on Quantum Computing and Engineering, QCE 2023

作者： Baral, Biswaraj Majumdar, Reek Bhalgamiya, Bhavika Roy, Taposh Dutta Quantum Computing Group Qausal Ai San RamonCA United States Clemson University Civil Engineering Department ClemsonSC United States Mississippi State University Dept of Physics and Astronomy MS United States Kaiser Permanente San RamonCA United States

ISBN: (纸本)9798350343236

this study presents an effective analysis of a hybrid transfer learning algorithm in medical image processing, specifically focusing on histopathological cancer detection. the research involves a two-part approach. First, a hybrid model was created, combining classical and quantum units for classifying images into cancerous and non-cancerous cells. Second, the performance of this hybrid model was compared with traditional models, quantum simulators like Pennylane, and IBM's real quantum computers (Lima, Quito, Belem, and Jakarta), considering factors such as the number of qubits, Variational Quantum Classifier (VQC), and number of images. the objective was to aid users in selecting the optimal combination of classical and quantum units for accurate classification. the study found that the hybrid transfer learning model, when used with the Pennylane quantum simulator, yielded slightly higher accuracy than hybrid models implemented with IBM's quantum computers. © 2023 IEEE.

关键词： Diseases

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application research of interior space design elements based on image processing 4

Application research of interior space design elements based...

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4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Zhang, Xinchao Liu, Yan Shandong Business Institute Yantai264670 China

In order to quantitatively analyze the application performance of elements in interior space design, a detection method of texture visual features of elements distribution images in interior space design based on image processing and texture edge contour features detection is proposed. A multi-resolution visual information collection model for detecting the texture visual characteristics of element distribution images in indoor space design is constructed, and a visual characteristic identification model for element application configuration in indoor space design is established. Multi-texture grid block area combination design method is adopted to realize the fusion of element application configuration visual images in indoor space design. through the edge feature detection method of texture grid area, Establish a visual parameter identification model of element application configuration in interior space design, extract the visual information feature quantity of element application configuration in interior space design, adopt Harris corner feature matching method, realize distributed detection of texture edge contour features of element application configuration visual features in interior space design, and fuse detection of high-resolution texture information through combined filtering and collaborative filtering. the collection and optimization of visual features of element application configuration in interior space design are realized, and the distributed detection algorithm of texture edge contour features is adopted to improve the convergence level of detection of texture visual features of element distribution images in interior space design, and the application of elements in interior space design based on image processing is realized. the simulation results show that this method has good resolution and strong visual expression ability in extracting the texture visual features of element distribution images in interior space design, which improves the qua

关键词： Collaborative filtering

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Experimental Analysis of Malware Detection and Classification System using Intelligent Deep learning Methodology 4

Experimental Analysis of Malware Detection and Classificatio...

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4th IEEE international conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2023

作者： Indumathi, K. Krishnaprasanna, R. Chamundeeswari, G. Preetha, M. Tamilvani, K. Senthilkumar, B. Rajalakshmi Engineering College Department Of It Tamil Nadu Chennai India Sathyabama Institute Of Science And Technology Department Of Ece Tamil Nadu Chennai India Saveetha Engineering College Department Of Ece Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department Of Cse Tamil Nadu Chennai India Simats Saveetha School Of Engineering Department Of Biosciences Tamil Nadu Chennai India V.S.B. Engineering College Department Of Eee Tamil Nadu Karur India

ISBN: (纸本)9798350384628

computer system security relies heavily on the ability to identify and eliminate malware. Signature-based approaches are widely used, although they are not able to effectively identify zero-day assaults or polymorphic infections. this is why there is a demand for detection methods based on deep learning and categorization. computers, Smartphones, and apps are all fair game for malicious software. Malware is software that has to be watched out for since it might be a hazard for both computer users and internet-based systems. Malware was developed with the intention of stealing sensitive data from a user's computer or taking remote control of a user's device. Malware is software designed to disrupt or even destroy data on a user's computer. Malicious software can enter a computer in a number of ways, one of which occurs over a network that already has the virus installed. this project will investigate and deploy high-precision classification models using deep learning technologies from the discipline of computational intelligence to enhance the efficiency and accuracy of malware detection. learning based Classification Scheme for Improved Malware Detection (LCSIMD) is a unique technique described in this work and utilized as the primary training model. the suggested LCSIMD framework is trained with malware visualization tools. Models with higher classification accuracy can be found by transforming malware features into images and then modifying their characteristics and input procedures;similarly, prototypes with proposed model can benefit from the use of suitable characteristics and preprocessing methods. the final step is to generate characteristics for network-generated instances, which improves the precision of small-scale training. Every one of us desire to utilize malware that impacts various devices as a teaching example, as described above. the output efficiency of the suggested technique is cross-validated with the classic Neural Network (NN) model to assess

关键词： Malware

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Hyperparameter Tuning for Edge-IIoT Intrusion Detection Using SMOTE 4th

Hyperparameter Tuning for Edge-IIoT Intrusion Detection Usi...

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4th international conference on Communication and Intelligent Systems, ICCIS 2022

作者： thiyam, Bidyapati Dey, Shouvik National Institute of Technology Nagaland Dimapur797103 India

ISBN: (纸本)9789819920990

As the amount of information on the computer network has grown enormously, intrusion detection systems have faced more difficulty dealing with vast-dimensional information that includes unnecessary and duplicate information. It takes time and effort to detect threats, which raises the alarming rate accurately. To deal with the high-class imbalanced problem and high-dimensionality issue, this paper uses SMOTE-Pearson’s correlation coefficient (PCC) with variance threshold to reduce the high-dimension features and solve the imbalance class issue. It uses an edge-IIoT dataset that is pre-processed for the use of machine learning classifiers. the results are calculated using various performance metrics showing great accuracy, precision, recall, and Matthews correlation coefficient (MCC) enhancement. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Hybrid machine learning Algorithm for Prediction of Malaria 4th

Hybrid Machine Learning Algorithm for Prediction of Malaria

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4th international conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Adamu, Yusuf Aliyu Singh, Jaspreet Department of Computer Science and Engineering GD Goenka University Sohna India

ISBN: (纸本)9789819914784

As malaria is a fatal disease that can occur anywhere, prompt diagnosis is crucial to stopping the disease in its tracks and reducing its overall impact. To better foresee future malaria epidemics, a hybrid machine learning model was created in this research. the model performance can be improved using various strategies, such as ensemble methods or fine-tuning the hyperparameters. Choosing a proper ensemble technique impacts the model accuracy. the performance of the ensemble model was measured using several well-known machines learning algorithms, such as Decision Tree, Support Vector machine, Naïve Bayes, K-Nearest Neighbors, and Random Forest. this methodology’s stacking strategy of ensemble technique allowed the integration of five separate algorithms. Compared to other machine learning classifiers, the results obtained using this ensemble method is superior with accuracy 92.8%, AUC 91%, recall 100%, precision 85%, F1-score 91%, specificity 100%, macro-average 94%, weighted average 98%, and error rate 5.4%. the relative importance of each variable and the degree of connection between them in explaining malaria prevalence are calculated. the results indicate that the hybrid approach is valuable for anticipating malaria outbreaks. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Research on the Development and Execution of Virtual Reality Merchandise Software Utilizing the Artificial Intelligence Deep learning Algorithm 4

Research on the Development and Execution of Virtual Reality...

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4th international conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023

作者： Qiu, Xiongwei Shi, Lianzhi Ren, Jiale Zhejiang Yixiong Intelligent Technology Co. Ltd. Zhejiang China Hangzhou Ptop Information Technology Co. Ltd. Zhejiang China

ISBN: (纸本)9798350341690

As a method of immersive interaction, virtual reality (VR) is rapidly permeating a variety of disciplines due to the continuous development of artificial intelligence and deep learning technology. this study seeks to investigate the design and implementation methodologies of Al-based deep learning-based interactive product software for virtual reality. the study first provides an overview of virtual reality technology and deep learning algorithms, and introduces their application prospects in interaction design. then, the study details the key technology of gesture recognition based on deep learning to enhance the naturalness and immersion of virtual reality interaction. In terms of software design, the study designed a prototype of a virtual reality interaction product that integrates the above techniques, and evaluated its performance and user experience through experiments. the experimental results show that the virtual reality interaction product based on AI deep learning algorithms has achieved significant improvement in terms of interaction effect and user satisfaction. In summary, this study provides useful guidance and reference for further promoting the development of virtual reality interactive products. © 2023 IEEE.

关键词： Software design

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Deep learning Model for 3D LiDAR Point Cloud Codec Based on Tensor 4

Deep Learning Model for 3D LiDAR Point Cloud Codec Based on ...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Chithra, P.L. Lakshmi Bala, S. University of Madras Department of Computer Science Chennai India

ISBN: (纸本)9798350306118

In recent years, deep learning techniques, especially autoencoders, have shown remarkable ability in learning compact and meaningful representations from raw point cloud data. In this paper, new approach is proposed to enhance the performance of autoencoders for 3D point cloud data by integrating tensor-based methods. By exploiting the inherent multidimensional nature of point cloud data, the proposed method uses tensor to capture complex spatial relationships and feature interactions. A special architecture is designed to effectively use tensor-based operations and allow networks to learn hierarchical representations of point cloud data. the approach includes two techniques: Tensor compression and deep feature learning. the tensor compression phase uses state-of-the-Art tensor decomposition algorithm to exploit the inherent redundancies and patterns within the 3D point cloud data. By converting the raw point cloud data into a compressed tensor format, it significantly reduces 39.94% which is 5:3 compressed form of 3D point cloud into its dimensionality while preserving important spatial relationships between points. this compression not only speeds up subsequent processing steps, but also makes the data easier to store and transfer. Compressed tensor representations serve as inputs to deep learning models specifically designed to extract salient features and meaningful representations from compressed data. It uses a multimodal neural network architecture with convolutional and iterative layers to capture both local and global patterns in point cloud data. the findings of the experiment show the superiority of the methodology in terms of both reconstruction accuracy and feature extraction compared with existing techniques. A proposed deep learning model based on Tensor (DLBT) is a Lossless compression method provides a solution for efficient data storage, transmission, and processing in 3D LiDAR applications. © 2023 IEEE.

关键词： Tensors

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Leveraging multi-modal knowledge graph for operation and maintenance knowledge management and application: a case study of bridge cranes 4

Leveraging multi-modal knowledge graph for operation and mai...

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Chang, Tianyu Chen, Yongming Shao, Wei Song, Meixi Li, Xinyu College of Mechanical Engineering Donghua University Shanghai China College of Computer Science and Technology Donghua University Shanghai China College of Science Donghua University Shanghai China

ISBN: (纸本)9798400709449

Currently, massive high-value operation and maintenance (O&M) data has been accumulated in the routine inspection of equipment. However, due to low informatization, different storage formats, and poor search efficiency, the intelligent O&M manner is impotent with such data, let alone supporting the rapid mastery and efficient utilization of new staff. therefore, in the case of bridge cranes, a multi-modal knowledge graph (MMKG)-based O&M knowledge management and application manner is proposed and implemented. Based on the existing paper-based bridge crane O&M documents and images of their working conditions, an MMKG for bridge crane O&M is first designed with all entities, relations, and attributions defined and related. then, based on the established MMKG, an intelligent quiz system is realized for training new staff in rapid acquainting with fault diagnosis of bridge cranes. Meantime, a personalized knowledge recommendation manner is hence enabled with the results of the quiz. To validate the performance of the proposed manner, a case study was conducted in a steel company in Shanghai. the result demonstrates the potential value delivered by establishing MMKG, intelligent quiz, and employee training applications, which brings managerial insight towards a human-centric intelligent O&M in Industry 5.0 era. © 2023 ACM.

关键词： Knowledge graph

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A Comparative Analysis on the E-learning System Maturity of Four HEIs Amidst the Pandemic for Government application for Distance learning Education 9

A Comparative Analysis on the E-Learning System Maturity of ...

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9th international conference on Business and Industrial Research, ICBIR 2024

作者： Galang, Devy M. College of Technology Lyceum of the Philippines University Manila Philippines

ISBN: (纸本)9798350383010

the study focused on the e-learning system maturity of the four campuses of the Lyceum of the Philippines University amidst the COVID-19 pandemic in the Philippines, anchored on Dr. Stephen Marshall's e-learning Maturity Model (eMM) version 2.0. Data were collected through Google Forms from students and faculty, selected using simple random sampling based on a set of criteria, who used e-learning systems during the First Semester of Academic Year 2022-2023. A 4-point Likert Scale metrics was used and data gathered were analyzed using Statistical Package for Social Science (SPSS) version 22. Responses were categorized as fully adequate, largely adequate, partially adequate, and not adequate. To test its normality, Kolmogorov-Smirnov was used. However, it was not satisfied, producing a non-normally distributed data set. thus, a non-parametric test called Kruskal Wallis was used to compare. the study revealed that there is high significant difference in the maturity of e-learning systems across four campuses on five key process areas: learning, development, support, evaluation, and organization. LPU-Batangas has the most mature e-learning systems, followed by LPU-Manila, LPU-Cavite, and LPU-Laguna. Consequently, a development program is proposed which may help the campuses continuously improve their e-learning systems and prepare the government's application for distance education program. © 2024 IEEE.

关键词： Adversarial machine learning

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Ensuring Privacy and Security of IoT Networks Utilizing Blockchain and Federated learning 10

Ensuring Privacy and Security of IoT Networks Utilizing Bloc...

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10th international conference on Future Internet of things and Cloud, FiCloud 2023

作者： Rashid, Md Mamunur Choi, Piljoo Lee, Suk-Hwan Platos, Jan Huh, Young Kwon, Ki-Ryong Pukyong National University Department of Artificial Intelligence Convergence Busan Korea Republic of Donga University Department of Computer Engineering Busan Korea Republic of VSB-Technical University of Ostrava Department of Electrical Engineering and Computer Science Ostrava Czech Republic Promotion Foundation Korea Smart Medical Device Industry Seoul Korea Republic of

ISBN: (纸本)9798350316353

the Internet of things (IoT) has become a game-changing technology, bridging the gap between the real and virtual worlds and allowing for smooth data transfer and communication between linked objects. the other two potential technologies are blockchain (BC) and artificial intelligence (AI), whose application areas are incredibly diverse and which may perform best when combined. Since some traditional machine learning (ML) techniques have limitations, this article proposed using distributed machine learning techniques such as federated learning and blockchain to build a more reliable and secure IoT network that will be better protected and less susceptible to outside intrusions. As an alternative to centralized cloud storage, we also recommended using decentralized data storage techniques like the InterPlanetary File System (IPFS) and Hyperledger Fabric (HLF). Additionally, as a proof of concept, we deployed our model using Ethereum Smart Contracts (SC). Using the well-known cybersecurity dataset known as Edge-IIoTset, we utilized both centralized and federated machine learning models to evaluate the efficiency of the suggested approach. the experimental results and successful deployment of Smart Contracts demonstrate that employing Blockchain and distributed storage systems is preferable for safeguarding IoT networks. © 2023 IEEE.

关键词： Blockchain

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