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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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An Optimized Grid Computing based Feature Selection approaches from Brain Image dataset using Pretrained models 1

An Optimized Grid Computing based Feature Selection approach...

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1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Nayak, Sradhanjali Biswal, Pradyut Kumar Mishra, Pravakar Pradhan, Sateesh Kumar Niis Institute of Business Administration Dept. of Computer Science Bhubaneswar India Iiit Bhubaneswar Dept. of Etc Bhubaneswar India Niis Institute of Business Administration Dept. of Computer Science and Engineering Bhubaneswar India Utkal University Dept. of Computer Science and Application Bhubaneswar India

ISBN: (纸本)9798350355093

The outcome of simulated experiments expressed the efficiency of grid-based algorithm in the domain of image processing in the context of Brain MRI Images for Tumor Detection. It provides a faster approach in detecting object and achieving higher rate of detection in comparison with existing algorithms for the similar purpose. The foremost goal of this work is improving the rate of detection by using grid based feature fusion approach with Genetic Algorithm(GA), Particle Swarm Optimization(PSO) and Scalp Swarm Optimization Algorithm. This technique produces efficient result due to hybrid fusion of gridded Images and optimization for exhaustive and precise image analysis. In the initial phase Each image is segmented into 4 grids. Pretrained models like Inception-V2, Resnet 50, Mobnetv2, VGG-16 are used to extract features before and after the images are gridded. The extracted features are analysed with classifiers such as Decision tree, Support vector Machine, Multiplayer and Self Attention Transformer that not only chooses the best set of features as well as best feature extractor. Among all these models the VGG-16 gives best result in terms of Accuracy, sensitivity, specificity and F-score. Hence VGG-16 is utilized as evaluation functions in the optimization Algorithms. The performance of the approach is measured by the values produced against the terms as precision, Recall, F-score, and accuracy rate. In the first phase of iteration, features extracted from each grid are assigned with initial weight. Optimization algorithms are used for optimizing the weight values in the combined features to bring best accuracy ratio. Grid based segmentation of Image stabilize the computation time thereby reduces the time complexity. Weighted sum Scalp Swarm Optimisation assigns best weighted values to segmentation parameters to get maximum performance. The experimental outcomes proves the efficiency of the proposed strategy. © 2024 IEEE.

关键词： Genetic algorithms

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Enhancing Security and Privacy in Health Care Using Cyber-physical Systems Through Machine Learning 8th

Enhancing Security and Privacy in Health Care Using Cyber-ph...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Takale, Dattatray G. Gawali, Piyush P. Deshmukh, Gopal B. Mahalle, Parikshit N. Mehta, Pradnya S. Kashid, Shraddha S. Sule, Bipin Derle, Deepak R. Department of Computer Engineering Vishwakarma Institute of Information Technology SPPU Pune India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology SPPU Pune India Department of Computer Engineering MIT ADT University Pune India and Humanities Vishwakarma Institute of Technology Pune India Department of Computer Science and Application KTHM College Nashik India

ISBN: (纸本)9789819766772

The importance of securing cyber-physical systems (CPS) is paramount to safeguarding the interconnected networks and devices, especially in sectors such as blockchain, health care, and the "Internet of Things" (IoT). The importance of authentication for users and devices in CPS security is highlighted in this paper. As a result of the intricate interconnectivity of networks, the integration of cyber-physical systems (CPS) in health care has revolutionized the delivery of medical services in an era of rapidly advancing technology. There are, however, serious concerns surrounding the privacy and security of sensitive healthcare data as a result of this integration. In this paper, a revolutionary approach to address these concerns is presented by utilizing machine learning techniques to address these problems. To enhance security measures within healthcare CPSs and protect privacy, Random Forest, a powerful ensemble learning algorithm, is employed as an integral part of the security measures. The proposed system utilizes a combination of anomaly detection, adaptive learning, and multi-attribute feature selection in order to identify potential security breaches while simultaneously ensuring patient confidentiality. As an additional security precaution, encryption protocols are implemented to ensure that the transmission of data is secure, preventing unauthorized access to the data as well. "As a comprehensive evaluation of the system's performance, a number of evaluation metrics are used, including Accuracy, Precision, Recall, F1 Score, and AUC-ROC". Based on these results, it is evident that this approach is effective in providing robust security and privacy protections in healthcare CPS, thereby establishing a new paradigm for delivering safe and confidential health care in the digital age. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cybersecurity

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Bioinspired Clustering in UWSNs Using Monarch Butterfly Optimization Compared with Ant Colony Optimization Algorithm

Bioinspired Clustering in UWSNs Using Monarch Butterfly Opti...

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

作者： Indira, G. Rani, Ruchika Chiranjevi, D. Husain, Saif O. Yogi, Rahul Nandhini, T.J. Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chandigarh Group of Colleges Department of Computer Application Chandigarh Engineering College Punjab Mohali India Aditya Engineering College Department of Computer Science & Engineering 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 Ies College of Technology Department of Computer Science & Engineering M.P. Bhopal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Science-SIMATS Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350390759

The Monarch Butterfly Optimization (MBO) algorithm for clustering in Underwater Wireless Sensor Networks (UWSNs) is investigated and compared to the performance of the Ant Colony Optimization (ACO) algorithm in this paper. As is evident from the outcomes depicted in the above graphs MBO consumes an average of 31% less energy than the ACO while lasting longer in the network. 7J or make a network get 790 round life cycle while ACO did 34. 5J and 730 rounds. The results indicate that MBO is more advantageous to cluster in the UWSNs making it a viable solution to boost up the network productivity and the overall energy utilization efficiency. © 2024 IEEE.

关键词： Ant colony optimization

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CryptoRecord: Advancing Electronic Medical Record (EMR) Security with Blockchain Technology

CryptoRecord: Advancing Electronic Medical Record (EMR) Secu...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Smitha G V Aishwarya Ghorpade K Asthik Nikita Yadav Masters of Computer Application Dayananda Sagar College of Engineering Bengalurru Karnataka India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

This Health and medicine are definitely important to human life in modern times. With many hospitals employing different Record Management Software (RMS), healthcare stakeholders have various alternatives when it comes to electronic patient information. Some store it locally in their databases; others use a cloud service provider. Patients do not have direct control over their own medical data because it is kept on a centralized system. Sharing information among the medical community (from one hospital to another) is a major challenge. Blockchain technology can be employed to ensure secure sharing of Electronic Medical Records (EMR). The Electronics Medical Record (EMR) is a digital representation of a patient's medical history, encompassing patient information, personal details, drugs, prescriptions, medical background, health history lab results, diagnostic tests, and reports such as X-rays. However, the current centralized storage of EMR limits patients' Patients lack autonomy over their own medical data and poses challenges for secure sharing among different healthcare providers. To address this issue, we propose a decentralized and secure approach using Interplanetary File Systems (IPFS) for storage and the RSA algorithm for record encryption. By utilizing blockchain technology and cryptography, our solution seeks to provide patients total ownership over their medical information while maintaining data integrity.

关键词： Data privacy Hospitals X-rays User experience Blockchains Encryption InterPlanetary File System History Electronic medical records Medical diagnostic imaging

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Investigating the Potential of Applying Permission Blockchain for IoT Networks 15

Investigating the Potential of Applying Permission Blockchai...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Manjunath, H.R. Moharana, Alok Kumar Infimum School of Engineering and Technology Department of Physics Karnataka Bangalore India Arka Jain University Department of Pharmacy Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

This paper investigates the capability of applying permission block chain for IoT networks. Permission block chain is a dispensed ledger device that uses a consensus-based totally method to shop and manipulate records. Such a technique offers a comfy and dependable platform for trustless environments, permitting a comfy, open, and immutable statistics infrastructure. Moreover, permission block chain can allow authentication of statistics, creating a less complicated way for trusted entities to accept as true with facts from untrusted assets, along with IoT networks. Moreover, the paper explores the possible packages and demanding situations of permission block chain in deploying at ease, dependable, and electricity-efficient IoT networks. Demanding situations together with scalability, interoperability, strength performance, privateers, and safety need to be addressed when it comes to the development of permission block chain answers for IoT networks. In the end, tips and answers are mentioned, along with proposals for building a hybrid permission layer for IoT networks. © 2024 IEEE.

关键词： Authentication Complicated Infrastructure Permission Scalability Situations

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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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Traffic Prediction Analysis (TPA) Using Machine Learning Methodologies 6

Traffic Prediction Analysis (TPA) Using Machine Learning Met...

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

作者： Solanki, Mohit Rawat, Vandana Pandey, Monika Singh, Neelam Aswal, Shobha Graphic Era Deemed to Be University Department of Computer Application Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India School of Computing Graphic Era University Dehradun India

ISBN: (纸本)9798350304480

A traffic prediction system might help drivers in making better travel choices. Minimizing carbon emissions, reducing traffic congestion, and improving the effectiveness of traffic management to deliver such traffic flow data is the main aim of traffic flow prediction. Prediction of traffic is essential in a system of intelligent transportation. Effective traffic forecasting can help with route design, vehicle dispatching, and reducing traffic congestion. Due to its incredible capacity to predict traffic patterns, a branch of machine learning which is also called 'deep learning,'has recently attracted a lot of attention. In this study, a comparative evaluation has been done to predict traffic flow based on deep learning. To prepare the stacked auto encoder model, basic traffic flow features are included. The goal of this study is to conduct a thorough screening for deep earning -based traffic forecast algorithms from multiple angles. This study recommends a deep learning method to predict urban traffic that combines data from twitter messages with traffic and weather data. The predictive model uses a deep Bi-directional long- short-term memory (LSTM) Stacked Auto Encoder (SAE) architecture for multi-step traffic flow prediction that was trained from tweets, traffic, and weather datasets. To support study in this area, it also gathers and arranges frequently available public datasets. © 2023 IEEE.

关键词： Long-short Tem Memory (LSTM Stacked Auto Encoder (SAE etc

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Activity Recognition System via Unification of CNN and SVM in Complex Domain

Activity Recognition System via Unification of CNN and SVM i...

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International Conference on Information Systems and computer Networks (ISCON)

作者： Parul Choudhary Pooja Pathak Abhishek Chaubey Department of Computer Application and Engineering GLA University Mathura

Human automatic activity recognition is an essential part of a little interactive application which involves human-being. The main disadvantage is the person’s diverse activities. These techniques provide high accuracy and pattern recognition. Deep learning methods succeed in human activity recognition (HAR); hence our goal is to combine Convolutional Neural Networks (CNN), which work with image data and extract features from images with Support Vector Machines (SVM), a shallow architecture that maps low-dimensional space to high-dimensional space. This paper uses an intelligent system algorithm to detect human activity and recognize its pattern. Cooking, cleaning, dancing, steering, and discussion activities are to be tested. Each class has 7480 images. Applying all preprocessing techniques, experimental results shows that the cleaning class gives the highest training and testing accuracy i.e., 98.78% and 97.51%. This method achieves the highest recognition accuracy with the lowest computational cost.

关键词： Support vector machines Training computer architecture Predictive models Feature extraction Cleaning Convolutional neural networks

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Research on Program Change Impact Analysis Methods Based on Software Networks

Research on Program Change Impact Analysis Methods Based on ...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Shiyong Li Fei Deng Xinglin Guo Zhenxiao Sun Zhuoliang Zou Institute of Computer Application China Academy of Engineering Physics Mianyang Sichuan China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

Aiming at the problem that the current program change impact analysis methods have coarse calculation granularity and without considering the actual situation of program change, results in overstating the impact propagation range and inability to quantify the degree of change impact, this paper proposes a program change impact analysis method based on the software network. Firstly, by combining the complex network theory to characterize the structural attributes and evolution patterns of software system, we abstract the software network morphology of system; Secondly, basing on the type of program change and the actual propagation of change impacts, two concepts of impact propagation decay rate and minimum threshold for impact marking are introduced to construct the simulation sub-model of impact propagation processes, enabling the analysis of the propagation range of program impacts; Thirdly, according to the structural properties of software network and the intrinsic attributes of nodes, the ripple degree simulation sub-model is constructed, and five quantization factors of the node's influenced degree are given to complete the quantitative assignment of the node's influenced degree; Finally, several open source software is used for application verification. Experimental results show that the proposed method aligns with reality in analyzing the propagation range of program change impacts, thus scientifically and reasonably reflecting the ripple effects of change impacts within the software system. At the same time, the method accurately and effectively quantifies the impact of program changes on other parts, thereby providing theoretical support for change impact analysis in software engineering.

关键词： Quantization (signal) Morphology Software quality Software systems Software Software reliability Stakeholders Security Open source software Software engineering

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