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Building Crack Detection Using CNN and Class Activation Mapping

Building Crack Detection Using CNN and Class Activation Mapp...

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Keerthana, Bodduru Devi, Jathuka Aruna Madhuri, Surisetty Rao, Marada Srinivasa Rao, Koduganti Venkata Geethika, P.S.S. Anil Neerukonda Institute of Technology and Sciences Department of Information Technology Andhra Prades Visakhapatnam India Andhra Prades Visakhapatnam India Vignan Institite of Engineering for Women Department of Information Technology Andhra Prades Visakhapatnam India MVGR College of Engineering Department of Information Technology Andhra Prades Vizianagaram India Guru Nanak Institutions Technical Campus Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331517953

As the population increases, further structures are being erected to meet the demands of the populace. Nonetheless, this surge in development presents the challenge of guaranteeing that each edifice fulfills the aspirations of its inhabitants. Identifying and rectifying damage or defects in structures can be challenging, particularly when conventional techniques typically necessitate comprehensive examinations to pinpoint issues. To address this difficulty, we are utilizing advanced technology such as Deep Learning models, particularly Convolutional Neural Networks (CNNs), along with approaches like Class Activation Mapping (CAM). These models utilize extensive datasets to learn and recognize patterns, enabling them to precisely identify specific forms of damage or defects in structures. We intend to forecast probable damage rates in advance through the application of these Deep Learning approaches. This proactive strategy enables us to avert catastrophic incidents by recognizing and resolving problems prior to their escalation. The great precision of these models ensures that buildings are safe and fulfill tenant expectations, hence enhancing the safety and reliability of structures within our communities. Our model attained an accuracy of 88%. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring Feature Extraction for Image Segmentation Methods in Critical Tumor Detection

Exploring Feature Extraction for Image Segmentation Methods ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Upadhyay, Ramakant Rastogi, Shreshta Bandhu Mehta, Deepak Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

this technical summary affords an exploration of function extraction for photo segmentation techniques to discover a critical tumor detection approach using forest Mapping. A segmentation technique is a critical step in medical imaging utility;its miles applied to extract and segment essential data from the photo. In this technical abstract, popular photo segmentation methods can be reviewed, emphasizing exploring and comparing function extraction techniques for powerful side-primarily based photo segmentation. Especially forest mapping strategies will be examined to recognize the potential benefits of using form-primarily based traits to facilitate tumor segmentation. Subsequently, vital programs in tumor detection could be discussed to verify the only characteristic extraction techniques from a clinical attitude. © 2024 IEEE.

关键词： emphasizing examined subsequently technical summary

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Reimagining Mutual information for Defense against Data Leakage in Collaborative Inference 38

Reimagining Mutual Information for Defense against Data Leak...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Duan, Lin Sun, Jingwei Jia, Jinyuan Chen, Yiran Gorlatova, Maria Department of Electrical and Computer Engineering Duke University United States College of Information Sciences and Technology Pennsylvania State University United States

Edge-cloud collaborative inference empowers resource-limited IoT devices to support deep learning applications without disclosing their raw data to the cloud server, thus protecting user's data. Nevertheless, prior research has shown that collaborative inference still results in the exposure of input and predictions from edge devices. To defend against such data leakage in collaborative inference, we introduce InfoScissors, a defense strategy designed to reduce the mutual information between a model's intermediate outcomes and the device's input and predictions. We evaluate our defense on several datasets in the context of diverse attacks. Besides the empirical comparison, we provide a theoretical analysis of the inadequacies of recent defense strategies that also utilize mutual information, particularly focusing on those based on the Variational information Bottleneck (VIB) approach. We illustrate the superiority of our method and offer a theoretical analysis of it. © 2024 Neural information processing systems foundation. All rights reserved.

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Dynamic Data Masking by Two-Step Encryption

Dynamic Data Masking by Two-Step Encryption

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2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023

作者： Adak, M. Fatih Kose, Zehra Nur Akpinar, Mustafa Sakarya University Computer Engineering Department Sakarya Turkey Computer and Information Science Higher Colleges of Technology Sharjah United Arab Emirates

ISBN: (纸本)9798350306590

Data masking and encryption are crucial techniques in ensuring the security and privacy of sensitive information. This provides a robust layer of protection by transforming data into an unreadable format using cryptographic algorithms. Data masking and encryption play vital roles in safeguarding sensitive data, preserving confidentiality, and maintaining the trust and integrity of data-driven systems. This study used the block cipher method to perform performance analysis with different block models with the AES algorithm. Subsequently, a dynamic data masking system was developed with double encryption. The analyses showed that the fastest encryption and decryption times were ECB, CFB, and CBC, respectively. However, the fact that ECB has low security and CBC is slow compared to others shows that CFB is the best choice among block models. In conclusion, CBC mode with the AES algorithm has proven to be a robust and widely adopted method for secure data encryption. Furthermore, with the increasing importance of data privacy and security, there will likely be continued efforts to bolster encryption protocols and standards. Overall, CBC encryption with the AES algorithm is expected to maintain its relevance and effectiveness in the future, providing a solid foundation for secure data transmission and storage. © 2023 IEEE.

关键词： Cryptography

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Implementing Machine Learning Techniques to Enhance Real-Time Data Analysis Services 3

Implementing Machine Learning Techniques to Enhance Real-Tim...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Jaisudha, J. Dhabliya, Dharmesh Narasimhayya, B.E. Singh, Akhilendra Pratap Sinha, Sanjay Kumar Vashisht, Nitish Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350319125

System learning techniques have emerged to automatically extract data from massive volumes of statistics and enable predictive analytics. Gadget-gaining knowledge has, in particular, been applied to records-driven real-time analytics offerings to facilitate accurate, short, and efficient selection-making. This paper discusses how machine studying strategies can be implemented to improve the accuracy and speed of real-time facts analysis. Conventional statistics analysis models are regularly misguided because of latency troubles and the absence of contextual statistics. Machine getting-to-know techniques have been followed to lessen latency issues, contain contextual facts, and enhance the accuracy of predictions. The paper offers a top-level view of some commonly used machine learning algorithms, such as supervised getting to know, unsupervised mastering, decision timber, neural networks, and support vector machines. It also explains how they can beautify actual-time analytics services. It also covers how strategies, including regularization and dimensionality discount, may be applied to enhance the model's overall performance. The paper gives case research of real-time analytics packages in finance, healthcare, and retail to illustrate the efficacy and added cost of gadget learning in facts analytics offerings. © 2023 IEEE.

关键词： Support vector machines

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Image Encryption Using Chaotic Logistic and DNA Encoding 5

Image Encryption Using Chaotic Logistic and DNA Encoding

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kumar, G. Kiran Anila, M. Chandrika, C. Priyanka, M. Jayaram, D. Mohan, A. Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Gandipet Telangana Hyderabad India Chaitanya Bharathi Institute of Technology Department of Information Technology Gandipet Telangana Hyderabad India

ISBN: (纸本)9798350389609

With a rapid development in technology and digital communication, there is a need of high speed with strong secured systems. In every sector, data is communicated in various formats, among them, images are one. It is crucial to protect the image data from unauthorized access from various sources like email spoofing and spamming, malicious software, unknown access through network, cloud, storage and other related activities. Therefore, securing our image from such unauthorized access is very important. Encryption is one method of achieving high security by shielding our data from unauthorized users. It is more difficult for attackers to decipher an image when Chaotic Logistic and DNA Encoding are used for image encryption. Using this encryption technique, each image that needs to be sent is split into parts, each of which is then encrypted and sent to the recipient. Consequently, an individual possessing only one or two portions of the image will be unable to utilize it. As a result, the goal of this strategy is to achieve high security and data confidentiality. In this research, Chaotic Logistic and DNA Encoding method is used to provide security for image data that outperformed when compared to other methods. © 2024 IEEE.

关键词： Encoding (symbols)

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Analyzing Multilayer Perception Architectures for Reinforcement Learning

Analyzing Multilayer Perception Architectures for Reinforcem...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Upadhyay, Ramakant Pipersenia, Arun Kumar Nidhya, M.S. Sanskriti University Department of Computer Science Engineering Uttar Pradesh Mathura India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

Reinforcement learning (RL) is a famous and influential technique for fixing complicated problems in synthetic intelligence. But it typically calls for considerable records and computational assets to be effective. A key to a hit RL is a suitable illustration of the environment country records. A famous approach to that is using multilayer notion (MLP) architectures. In this paper, we recognize MLP architectures as an essential constructing block for plenty of RL algorithms. We examine the effectiveness of MLP architectures for RL and present processes to improve their overall performance. First, we recommend a Multi-Layer Reinforcement gaining knowledge of (the MLRL) approach, wherein the MLP structure is included within the RL policy shape. 2d, we inspect an Ensemble of MLPs method, which combines a couple of MLPs into an unmarried RL policy. We practice each of these strategies to select RL duties and problem domains and display that they could result in stepped-forward learning performance. Our outcomes advice that MLP architectures provide a powerful illustration for reinforcement getting to know and that the MLRL and Ensemble processes can similarly improve the performance of those architectures. © 2024 IEEE.

关键词： architectures computational environment influential Reinforcement

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Protecting Data Mobility in Cloud Networks Using Metadata Security

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computer Systems Science & engineering 2022年第7期42卷 105-120页

作者： R.Punithavathi M.Kowsigan R.Shanthakumari Miodrag Zivkovic Nebojsa Bacanin Marko Sarac Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Department of Computer Science and Engineering School of ComputingSRM Institute of TechnologyKattankulathur CampusChennai603203India Department of Information Technology Kongu Engineering CollegeErode638060India Singidunum University Belgrade11000Serbia

At present,health care applications,government services,and banking applications use big data with cloud storage to process and implement *** mobility in cloud environments uses protection protocols and algorithms to secure sensitive user ***,data may have highly sensitive information,lead-ing users to consider using big data and cloud processing regardless of whether they are secured are *** to sensitive data in cloud systems produce high risks,and existing security methods do not provide enough security to sensitive user data in cloud and big data *** present,several security solu-tions support cloud *** of them include Hadoop Distributed File Sys-tem(HDFS)baseline Kerberos security,socket layer-based HDFS security,and hybrid security systems,which have time complexity in providing security ***,mobile data security algorithms are necessary in cloud environ-ments to avoid time risks in providing *** our study,we propose a data mobility and security(DMoS)algorithm to provide security of data mobility in cloud *** analyzing metadata,data are classiﬁed as secured and open data based on their *** data are sensitive user data,whereas open data are open to the *** the basis of data classiﬁcation,secured data are applied to the DMoS algorithm to achieve high security in *** pro-posed approach is compared with the time complexity of three existing algo-rithms,and results are evaluated.

关键词： Data mobility data security cloud computing big data DMoS algorithm

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Enhancing Smart City and IoT Solutions with Deep Residual Bi-LSTM Model

Enhancing Smart City and IoT Solutions with Deep Residual Bi...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Madumidha, S. Chander Rao, S. Poorna Yadav, Sameer Hashmi, Syed Ghyasuddin Ahmad, Jorair Syed, Ziauddin Sri Krishna College of Technology Department of Information Technology Tamilnadu India Geethanjali College of Engineering and Technology Eee Department Telangana Hyderabad India University of Allahabad Department of Commerce and Business Administration Uttar Pradesh Prayagraj India College of Computer Science & Information Technology Jazan University Department of Information Technology & Security Jazan Saudi Arabia Information Technology & Security College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia College of Computer Science and Information Technology Jazan University Department of Computer Science Jazan Saudi Arabia

ISBN: (纸本)9798350300857

There is a limitless supply of useful information and services made available by IoT devices connected to the internet. The Internet of Things (IoT) refers to the vast network of interconnected devices seen in a smart city. The scattered and dynamic natures of entities such as sensing, actuating devices, humans, and computing resources make it challenging to design applications in such large-scale IoT systems, despite the promise nature of the new breed of data and resources. In this research study, the proposed approach takes a coordinated look at the process of creating Internet of Things apps for use in Smart Cities. The proposed approach demonstrates the need for a distributed coordination model capable of monitoring such a vast number of independently operating parts while constructing Smart City IoT applications. Preprocessing, feature selection, and model performance evaluation are all used in the suggested method. Normalization and standardization are used during preprocessing. It uses entropy HOA for feature selection. The model's effectiveness is measured with Deep Residual Bi-LSTM. When compared to BiLSTM and Residual LSTM, two existing approaches, the suggested methodology performs admirably. © 2023 IEEE.

关键词： Standardization

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An Algorithm for Modified Minimum Length Sliding k Transmitter Problem 10

An Algorithm for Modified Minimum Length Sliding k Transmitt...

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10th International Conference on Networking Systems and Security, NSysS 2023

作者： Al-Mahmud, Mohammad Saidur Rahman, Md. Graph Drawing and Information Visualization Laboratory Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka1205 Bangladesh

ISBN: (纸本)9798400708787

Advancement in wireless technology, artificial intelligence and robotics introduces different problems on coverage or visibility in different architectural shapes. Usually, architectural shapes are represented by two dimensional polygons. Such two dimensional polygons are partitioned using various partitioning techniques to solve those coverage or visibility problems. One such partitioning technique uses reflex vertices of orthogonal polygons. We establish some relations among different components of an orthogonal polygon after it is being partitioned by reflex vertices. There are many variants of each visibility or coverage problems in wireless technology. One such variant is minimum length sliding k transmitter problem. Such transmitters can travel back and forth along the boundary of an orthogonal polygon and can modeled as an omnidirectional wireless modem, transmit signal orthogonally and can penetrate through k walls (k is a fixed integer and k > 0) to reach the client. Let s be an orthogonal line segment where the k transmitter travels back and forth. A point p is covered by the guard if there exist a point q ĝ s such that is a line segment normal to s and at most k intersections with the boundary walls of the polygon. The objective is to minimize the sum of lengths of the sliding k transmitters to cover the entire polygon. In this paper we consider a modification in the above problem where instead of omnidirectional k transmitters we consider k transmitters which can transmit and receive signal orthogonally inside the polygon. The objective of the problem remains same and we termed this problem as modified minimum length sliding k transmitter problem. We develop an algorithm for this modified problem which finds the minimum length cover in O(n2) time. © 2023 ACM.

关键词： Computational geometry

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