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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Ravinder, Bathini Seeni, Senthil Kumar Prabhu, V.S. Asha, P. Maniraj, S.P. Srinivasan, C. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Cognizant Technology Solutions Buffalo Grove Department of Mobile Application Development Illinois United States R.M.D. Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Data mining on the web has developed into a simple and crucial tool for finding relevant information. When it comes to file transfers, the World Wide Web is the user's first choice. Finding useful information and trends in the ever-expanding amount of data available online is becoming ever more challenging and time-consuming. When dealing with massive amounts of textual data often seen online, there are a number of benefits to using the Naive Bayes method for web data mining. Web data mining using the Naive Bayes algorithm seeks to mine massive amounts of textual material on the web for useful patterns, insights, and knowledge. Text classification and categorization tasks are the most common uses of the Naive Bayes method in online data mining. Expert and user-requested data may be difficult to mine from the sea of unorganized and contradictory material that is the World Wide Web. Relevant data (hyperlinks, contents, web use records) is extracted from the web using a variety of mining methods. Internet-centric data mining is a subfield of data science. Structure mining, content mining, and use mining are the three main categories of online data mining. Each of these categories employs a unique set of methods, instruments, strategies, and Naïve Bayes algorithm to mine the web's vast data stores for useful insights. The results show the density and velocity of web mining using Naïve Bayes algorithm. © 2024 IEEE.

关键词： Data mining

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Role of Neural Network, Fuzzy, and loT in Integrating Artificial Intelligence as a Cyber Security System 6

Role of Neural Network, Fuzzy, and loT in Integrating Artifi...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Kumar, Rakesh Pokhariya, Hemant Singh Rao, A Kakoli Mayuri, K. Kumar, Amit Kumar, Sajeev Rana, Ajay GLA University Department of Computer Engineering & Application Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lloyd Institute of Engineering and Technology Greater Noida India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India Lloyd Law College Greater Noida India Amity University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

The massive network of electronically connected devices that is referred to as the 'Internet of Things'is the primary component of this system. These interconnected devices collect vital information that might have repercussions not only for the administration of the corporation, but also for society as a whole and the environment in general. The exponential growth of loT applications has corresponded with an increase in people's concerns over the privacy and security of their data When it comes to the most cutting-edge technological breakthroughs in the field of cybersecurity, artificial intelligence (AI) is now in the driver's seat at this point. AI is used to develop the complicated algorithms that are required to defend networks and systems, such as the gadgets that make up the Internet of Things (loT). As a consequence of this, having intelligent security solutions that are dependable, decentralized and readily available is more important than it has ever been. No matter how large the dataset is, the machine learning algorithm is unable to manage its diversity. This research takes a multi-layered approach to cybersecurity, which is necessary for protecting the TL of the Internet of Things (IoT), and it is one of the goals of this study. We recommended using a technique known as Elfers-Sugano Fuzzy and Trust-based Neural Networks (ESFTNN), which makes it possible to use three other approaches besides the one that is traditionally used in order to strike a healthy balance between the aforementioned factors. The Elfers Probability Sensing (EPS) Model is the first of its kind to take into account the extent to which any sensor connected to the Internet of Things is protected. By proportionately spreading data between nodes, Sugano Fuzzy Processing, which does not incorporate defuzzification, controls the amount of energy used. © 2023 IEEE.

关键词： Artificial intelligence Artificial neural networks Cyber Security System Internet of Things Renewable energy sources

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Detection of Gastric Cancer in Endoscopic Images Using Deep Convolutional Neural Networks

Detection of Gastric Cancer in Endoscopic Images Using Deep ...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Pauranik, Abhinav Nishanthi, R. Ramesh, U Veera Riad Al-Fatlawy, Ramy Kaur, Jasmeet Gandhi, Vaibhav C. Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Aditya Engineering College Department of Computer Science & Engineering Surampalem India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Department of Computer Engineering Gujarat India

ISBN: (纸本)9798350390759

In finding features of gastric cancer in endoscopic images, difficulties arise from early manifestations and variance in the appearance of lesions. The objective of this study is to design an automobile deep convolutional neural network (CNN) that would help enhance the rate of accurate diagnosis of gastric cancer. In the current work, using the dataset of endoscopic images reviewed by senior gastroenterologists, transfer learning is applied on the base of the ResNet-50 based deep CNN the result of data augmentation is extended. By evaluating the model on measures of training, validation, and testing sets, high performance has been realized as depicted by accuracy of 87. 4%, precision of 87. 5%, recall of 87%, and f1-score of 87%. Research on the comparison of the proposed method with the conventional CAD systems and machine learning models confirmed the efficiency of the deep learning method. The utilization of Grad-CAM visualizations brought interpretability in improving match between regions of focus and clinically relevant areas therefore improving the trust in the model among the clinicians. To foster efficiency, the implementation incorporated Python and deep learning tools, including TensorFlow, the model of which was trained on GPUs. Thus, this paper demonstrates the feasibility of the studied CNNs in promoting the early diagnosis of gastric cancer, suggesting the adaptation of AI tools as complementary tools for clinicians in everyday practice. Future studies should follow up on how false negative results may occur;additionally, future studies should devise better approaches on how to increase the generalizability of these models across different patients. © 2024 IEEE.

关键词： Convolutional neural networks

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HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level 61

HAHE: Hierarchical Attention for Hyper-Relational Knowledge ...

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61st Annual Meeting of the Association for Computational Linguistics, ACL 2023

作者： Luo, Haoran Haihong, E. Yang, Yuhao Guo, Yikai Sun, Mingzhi Yao, Tianyu Tang, Zichen Wan, Kaiyang Song, Meina Lin, Wei School of Computer Science Beijing University of Posts and Telecommunications China School of Automation Science and Electrical Engineering Beihang University China Beijing Institute of Computer Technology and Application China Inspur Group Co. Ltd. China

ISBN: (纸本)9781959429722

Link Prediction on Hyper-relational Knowledge Graphs (HKG) is a worthwhile endeavor. HKG consists of hyper-relational facts (H-Facts), composed of a main triple and several auxiliary attribute-value qualifiers, which can effectively represent factually comprehensive information. The internal structure of HKG can be represented as a hypergraph-based representation globally and a semantic sequence-based representation locally. However, existing research seldom simultaneously models the graphical and sequential structure of HKGs, limiting HKGs' representation. To overcome this limitation, we propose a novel Hierarchical Attention model for HKG Embedding (HAHE), including global-level and local-level attention. The global-level attention can model the graphical structure of HKG using hypergraph dual-attention layers, while the local-level attention can learn the sequential structure inside H-Facts via heterogeneous self-attention layers. Experiment results indicate that HAHE achieves state-of-the-art performance in link prediction tasks on HKG standard datasets. In addition, HAHE addresses the issue of HKG multi-position prediction for the first time, increasing the applicability of the HKG link prediction task. Our code is publicly available. © 2023 Association for Computational Linguistics.

关键词： Knowledge graph

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Geospatial Object Detection in Hyperspectral Imagery Using Spectral-Spatial Networks

Geospatial Object Detection in Hyperspectral Imagery Using S...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Prabakar, D. Pranavan, S. Kumbhkar, Makhan Sah, Swati Patil, Harshal Firos, A. Computer Science and Engineering Karpagam College of Engineering Coimbatore India Dhanalakshmi Srinivasan College of Engineering Department of Civil Engineering Navakkarai Coimbatore India Indian Institute of Soybean Research Department of Computer Application Indore India School of Set Sharda University Greater Noida India Pune India Rono-Hills Department of Computer Science and Engineering Arunachal Pradesh Doimukh India

ISBN: (纸本)9798350394580

By recording a wide range of spectral bands across the electromagnetic spectrum, hyperspectral imagery (HSI) delivers extensive information. Due to the complicated interaction between spectral and spatial properties, extracting meaningful geographical information from HSI is difficult. This article uses Spectral-Spatial Networks to detect geographical objects in hyperspectral data. The proposed deep learning system blends spectral and spatial information to improve object discrimination in complicated scenarios. SSN uses convolutional neural networks (CNNs) and spectral attention mechanisms to adaptively weigh spectral bands and capture spatial correlations. A large dataset of annotated hyperspectral photography with ground truth object identification trains the SSN. The collection includes different lighting, topography, and item scales. The object detection task is fine-tuned after pre-training on a large HSI dataset. Experimental results on benchmark hyperspectral datasets show that the SSN outperforms state-of-the-art approaches. Precision, recall, and F1-score for numerous object classes reveal that the suggested technique can distinguish objects in complicated and congested situations. The suggested SSN framework is also resilient to illumination, climatic conditions, and sensor properties, making it suitable for real-world applications. The SSN architecture's scalability is further examined, showing its suitability for heterogeneous HSI datasets with different spectral resolutions. © 2023 IEEE.

关键词： Environmental Monitoring Geospatial Object Detection Hyperspectral Imagery Remote Sensing Spectral-Spatial Networks

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Extrapolative Preservation Management of Medical Equipment through IoT 1

Extrapolative Preservation Management of Medical Equipment t...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Tripathi, Rajeev Mishra, Vinay Kumar Maheshwari, Himani Tiwari, Raj Gaurang Agarwal, Ambuj Kumar Gupta, Ashulekha School of Management Sciences Department of Computer Application and Sciences Lucknow India School of Computer Application Babu Banarasi das University Lucknow India School of Computing Graphic Era Hill University Uttrakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350330915

The healthcare industry is primarily driven by technological innovations as they are a significant source of inspiration to provide the best patient care. To provide patients with prompt, high-quality care, medical device manufacturing has grown at an unprecedented rate. Hospitals should use the best maintenance techniques to increase the life of their equipment while trying to reduce maintenance costs and efforts as medical device usage increases. This paper proposes a predictive maintenance method (PdM) to assist in identifying critical equipment failures with diverse and frequent failure modes. The proposed method is based on the understanding of the physics of the incident, real-time data collection of relevant parameters using Internet of Things (IoT) technology, and application of machine learning algorithms to predict and classify the condition of the device as good and bad. To demonstrate the feasibility and effectiveness of moving from traditional maintenance to PdM, an economic analysis should be provided. The objective of this study was to determine the challenge of medical device maintenance using an Internet of Things (loT) enabled automated health monitoring system for devices that generate large amounts of data. real-time data, at scale, in healthcare organizations. © 2023 IEEE.

关键词： Failure Management Internet of things IoT Medical Equipment Maintenance Predictive Maintenance Sensors

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Smart Home Personal Assistants: Fueled by Natural Language Processor and Blockchain Technology 2

Smart Home Personal Assistants: Fueled by Natural Language P...

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2nd International Conference on Interdisciplinary Cyber Physical Systems, ICPS 2022

作者： Ansar, Syed Anas Jaiswal, Kriti Aggarwal, Shruti Shukla, Shruti Yadav, Jaya Soni, Nupur Babu Banarasi das University Department of Computer Application Lucknow India University of Lucknow Department of Computer Science Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India

ISBN: (数字)9781665470223

ISBN: (纸本)9781665470223

With the ever-increasing cost of livelihood, technological intervention is necessary to keep costs under control. Hence, smart home technology can be considered as a well-established and entrenched region of interest. In the current scenario, research subsidizes present-time houses, enabling the user to design and manage a smart home to conserve energy while allowing for more autonomous applications. A voice-activated home automation system integrates the Internet of Things, Artificial Intelligence, Blockchain, and Natural Language Processing to deliver a cost-effective and efficient method of interacting with household equipment. The primary objective of this paper is to assess SPAs using blockchain technology and a natural language processor (NLP), as well as the design of a SPA based on the internet of things. Applicable cyber threats and countermeasures are also addressed in this article. Using Yule's coefficient of association, a case study was undertaken to evaluate the association between privacy and security threats posed by this modern technology. The findings show that privacy and security are positively related. © 2022 IEEE.

关键词： Blockchain

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Smart Firefighting Early Warning and Evacuation System Based on Edge Computing with Led Dynamic Indicator Devices 6

Smart Firefighting Early Warning and Evacuation System Based...

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6th International Conference on Communications, Information System and computer engineering, CISCE 2024

作者： Yu, Liang Lai, Yangbing Mo, Jiayu Zeng, Yuze Tan, Guoshen Xu, Haonan College of Computer Guangdong University of Science and Technology Dongguan China AIoT Edge Computing Engineering Technology Research Center of Dongguan City Guangdong AIoT Application Innovation Joint Laboratory Dongguan China

ISBN: (纸本)9798350353174

The current standard practice for building fire safety relies heavily on regular lighting, emergency lighting, and smoke sensors. The management and control centers for these systems are typically separate and independent, resulting in a bulky and expensive local control system that is difficult to use and manage. Additionally, the traditional fixed instructions in fire protection systems do not allow for adaptive planning of optimal escape routes based on the location of the fire, leading to delays in evacuation and ultimately causing significant casualties. This paper presents the design of a smart fire warning and evacuation system based on edge computing utilizing LED dynamic indication devices. The system integrates LED dot-matrix technology to create a smart indicator and emergency light with dynamic adaptive indication capabilities. Additionally, it includes temperature, humidity, and smoke sensors to monitor the building's environmental conditions. Through Mesh wireless networking, time-series sensing data is aggregated to the edge gateway, where a miniature machine learning model trained by the Edge Impluse (EI) platform performs edge computing for predicting and reasoning about fire situations. The data is then uploaded to a cloud server and forwarded to mobile users via Cat.1 communication protocol for real-time monitoring. The system also incorporates an improved ant colony algorithm for automatic execution in the cloud and allows manual commands from mobile terminals to control adaptive and dynamic indication of indicator lights and emergency lights, guiding building personnel to the safest evacuation path. System testing demonstrates a high accuracy rate of 95% for fire prediction and inference, with an average inference time of less than 1 ms. This system provides a novel approach to adaptive building fire prediction and evacuation, enhancing safety measures for orderly and secure evacuations. © 2024 IEEE.

关键词： Ant colony optimization

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Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering

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IEEE Transactions on Computational Social Systems 2023年第3期10卷 910-921页

作者： Deng, Ping Li, Tianrui Wang, Hongjun Wang, Dexian Horng, Shi-Jinn Liu, Rui Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Computing and Artificial Intelligence Chengdu611756 China National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan North Sichuan Medical College School of Preclinical Medicine Department of Computer and Mathematics Nanchong637100 China

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed;and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the l1-norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance. © 2014 IEEE.

关键词： Matrix algebra

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Bitcoin-Blockchain Technology: Security Perspective 3

Bitcoin-Blockchain Technology: Security Perspective

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： Ansar, Syed Anas Arya, Swati Aggrawal, Shruti Yadav, Jaya Pathak, Prabhash Chandra Babu Banarasi Das University Department of Computer Application Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Patiala India University of Lucknow Department of Computer Science Lucknow India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Nowadays, Blockchain technology has also drawn mammoth attention as it is currently triggering multiple projects. This technology is the prevalent technology that allows Bitcoin to operate. Bitcoin has already cemented itself as a popular virtual currency, and it will soon become a pressing requirement for citizens. Furthermore, with digital currencies proliferating all over the world, security must not be overlooked, especially when there have been numerous attacks and security vulnerabilities on bitcoin's blockchain. However, the lucrative system has drawn opponents and attackers who are continually attempting to exploit flaws in the technology in order to get profit. This paper contemplates the bitcoin's transaction process, with a focus on obscuring the true identities of clients and their coins prior to transacting, and then elucidating on the following: SHA-256 function, the primary threats to Bitcoin's security, along with some countermeasures to be undertaken. © 2022 IEEE.

关键词： Blockchain

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