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Seismic Performance Assessment of Reinforced Concrete Frames: Insights from Pushover Analysis
This paper offers a comprehensive exploration of the seismic response of Reinforced Concrete (RC) frames examined through pushover analysis. The frames analyzed are designed as per IS 13920 and IS 456 for different levels of earthquake intensities and different levels of axial loads. Nonlinear analysis techniques have gained prominence in assessing the response of RC frames, especially when subjected to extreme loading events or when accurate predictions of structural behavior are required beyond the linear elastic range. The study aims to delve into the structural behavior of RC frames under seismic influences, employing pushover analysis as the principal analytical tool. With a focus on assessing the effectiveness and reliability of pushover analysis, the research endeavors to elucidate the seismic performance of RC frames while considering their response to different seismic zones and axial loading scenarios. The methodology involves conducting a series of pushover analyses on RC frames using advanced structural analysis software. The results obtained are meticulously analyzed to discern the shear capacities and ultimate displacements of the frames, by investigating the displacement versus shear capacity relationship across varying seismic zones and axial loading scenarios. Through this comprehensive investigation, the paper aims to enhance our understanding of the seismic behavior of RC frames and will provide valuable insights for seismic design. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Seismic Activity-based Human Intrusion Detection using Deep Neural Networks
Human intrusion detection systems have found their applications in many sectors including the surveillance of critical infrastructures. Generally, these systems make use of cameras mounted on strategic locations for surveillance purposes. Cameras based detection systems are limited by line-of-sight, need regular maintenance and dependence of electricity for operations. These are all detrimental to the efficiency of these detection systems, especially in remote locations. To overcome these challenges, intrusion detection systems based on seismic activities have been in use. The seismic activities collected through geophones from the human footfalls can act as the input for these detection systems. This also poses a challenge as the data generated by the geophones for the seismic activities produced from footsteps are not always identical and hence not accurate. In this proposed work, a Deep Neural Network based approach has been used on the dataset collected from the geophones to effectively predict the presence of humans. The results gave a success rate with 94.86% accuracy with testing data and 92.00% accuracy with real-time data with the geophones deployed on an area covered with grass. 2022 IEEE. -
Segregating direct and indirect dimensions in ecosystem services valuation: The case of a coastal wetland ecosystem of south india
This paper provides insights into the multiple (direct and indirect) benefits of Kuttanad coastal wetland ecosystem in Kerala. Total annual direct ecosystem services generated from the wetlands are INR 8.45 billion or USD 0.11 billion per annum at 2020 prices. The estimates of the case study indicate that the annual value of indirect ecosystem services is thrice of direct provisioning services (Rs 22.52 billion or USD 0.31 billion per annum at 2020 prices). The valuation study would improve the knowledge and awareness of economic importance of wetland ecosystems among the various stakeholders including the policy makers of the society and their sustainable management to benefit the society. 2021 Ecological Society of India. All rights reserved. -
Segmentation technique for medical image processing: A survey
Segmentation is one of the popular and efficient technique in context to medical image analysis. The purpose of the segmentation is to clearly extract the Region of Interest from the medical images. The main focus of this paper is to review and summarize an efficient segmentation method. While doing the comparison study on segmentation methods using the Support Vector Machine, K-Nearest Neighbors, Random Forest and the Convolutional Neural Network for medical image analysis identifies that Convolutional Neural Network works efficiently for doing in-depth analysis. The Convolutional Neural Network can be used as segmentation technique for achieving the high accuracy on medical image analysis. 2017 IEEE. -
Segmentation of ancient and historical gilgit manuscripts
The Gilgit manuscripts belong to fifth century A.D. and are oeuvre of texts which deal with Buddhist work. It is one of the oldest manuscripts in the world and is considered to be a milestone in the history of Buddhist works in India. It is a collection of both official and unofficial Buddhist works which are believed to have helped in the evolution of many literatures including Chinese, Japanese, and Sanskrit. Since this manuscript is almost seventeen centuries old it has not been able to fully decipher the text yet. It has been laminated by the National Archives of India which proves it is one of the most important literatures concerning India. In this paper, we perform character- based image segmentation on Gilgit manuscript in order to simplify and to better identify character in the image of the manuscript. The employed method gives an accuracy of nearly 87%. Springer India 2016. -
Segmentation and Recognition of E. coli Bacteria Cell in Digital Microscopic Images Based on Enhanced Particle Filtering Framework
Image processing and pattern recognitions play an important role in biomedical image analysis. Using these techniques, one can aid biomedical experts to identify the microbial particles in electron microscopy images. So far, many algorithms and methods are proposed in the state-of-the-art literature. But still, the exact identification of region of interest in biomedical image is a research topic. In this paper, E. coli bacteria particle segmentation and classification is proposed. For the current research work, the hybrid algorithm is developed based on sequential importance sampling (SIS) framework, particle filtering, and Chan–Vese level set method. The proposed research work produces 95.50% of average classification accuracy. 2019, Springer Nature Singapore Pte Ltd. -
Segmentation and identification of MRI Brain segment in digital image
Brain image segmentation is important in the area of clinical diagnosis. MRI Brain image segmentation is time consuming and there is always a chance of occurrence of error when the segmentation is done manually. It is always possible to detect the infected tissues easily in the current medical field. However, the accuracy and the characteristics of abnormalities of the tissues are not precise. In the past, many researchers have identified the drawbacks of manual segmentation and hence proposed the semiautomatic and fully automatic segmentation methods in the field of medical imaging. The amount of precision about the detection of defective tissues leads to acceptance of a particular image segmentation method. In this article three segmentation methods are hybridized to get the optimum extraction of the region of interest (ROI) in brain MRI image. Further, the region properties of segment is extracted and stored as knowledgebase. The proposed algorithm integrates multiple segmentation methods and identifies the Brain Outer layer in MRI image. This identification AIDS medical experts for optimum diagnosis of defective tissues in the brain. IAEME Publication. -
Segment Anything Model (SAM) to Segment lymphocyte from Blood Smear Images
Automated lymphocyte segmentation from smear images plays an important role in disease diagnosis and monitoring, aiding in the assessment of immune system function and pathology detection. This study proposes an approach for lymphocyte segmentation utilizing Segment Anything Model (SAM) which is a deep learning model. Our method leverages a pre trained SAM architecture and fine-tunes it on a custom dataset comprising smear images containing lymphocytes. The pretrained model's ability of versatile segmentation combined with fine-tuning on the specific dataset enhances its performance in accurately identifying lymphocyte boundaries. We evaluate the proposed approach on a diverse set of smear images, demonstrating its effectiveness in segmenting lymphocytes with impressive IOU score and Dice Score. SAM deep learning model, fine-tuned on custom datasets, holds promise for robust and efficient lymphocyte segmentation from blood smear images. 2024 IEEE. -
Security Threats and Privacy Issues in Cloud Data
The quick advancement of Web-based applications has led to a huge amount of information being scanned and gathered for business examination or scholarly research purposes, which may disregard individual protection. Organizations, industries and individuals data are at stake. In this paper, utilizing on the Web Personal Health Record (PHR) as contextual analysis, first demonstrate the need of inquiry ability approval that lessens the security introduction coming about because of the list items, and build up a versatile structure for authorized private keyword Search (APKS) over encoded cloud information. This particular model proposes two novel answers for APKS given on-going cryptographic crude, hierarchical predicate encryption (HPE). Our answers empower efficient multi-dimensional watchword looks with a run question, permit assignment and renouncement of hunt abilities. Additionally, the proposed system improves the question protection which conceals clients inquiry watchwords against the server. Actualize our plan on an advanced workstation, and exploratory outcomes exhibit its appropriateness for reasonable use. Privacy has seen advancement lately as information mining of the datasets in a dispersed huge information condition has turned into a successful worldwide business which is none other than data management or data analytics which ensures the security of data. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Security Standards in Serviced Apartments - With Special Reference to Bangalore and Chennai (South India) - An Analysis
International Journal of Research in Computer Application & Management, Vol-2 (11), pp. 130-135. ISSN-2231-1009 -
Security mechanisms in cloud computing-based big data
In the existent system, data is encrypted and stored when passed to the cloud. During any operations on the data, it is decrypted and then the computation is done. This decrypted data is vulnerable and prone to be misused. After the computations are done, the data and the result are encrypted and stored back in the cloud. This creates an overhead to the system as well as increases time complexity. With this chapter, the authors aim to reduce the overhead of the systems to perform repeated encryptions and decryptions. This can be done by allowing the computations to happen directly on the encrypted text. The result obtained by performing computations on encrypted data will be the same as the ones done on the original plain text. This new security solution is fully fit for processing and retrieval of encrypted data, effectively leading to the broad applicable project, the security of data transmission, and the storage of data. The work is secured further with additional concepts like probabilistic and time stamp-based encryption processes. 2021, IGI Global. -
Security mechanisms in cloud computing-based big data
In the existent system, data is encrypted and stored when passed to the cloud. During any operations on the data, it is decrypted and then the computation is done. This decrypted data is vulnerable and prone to be misused. After the computations are done, the data and the result are encrypted and stored back in the cloud. This creates an overhead to the system as well as increases time complexity. With this chapter, the authors aim to reduce the overhead of the systems to perform repeated encryptions and decryptions. This can be done by allowing the computations to happen directly on the encrypted text. The result obtained by performing computations on encrypted data will be the same as the ones done on the original plain text. This new security solution is fully fit for processing and retrieval of encrypted data, effectively leading to the broad applicable project, the security of data transmission, and the storage of data. The work is secured further with additional concepts like probabilistic and time stamp-based encryption processes. 2021, IGI Global. -
Security Intensification using Blockchain coupled with Internet of Things: Proposal, Challenges and Anatomization
Internet of things is an important part of our day-to-day life where all things are connected in the network with the internet. The number of devices linked to the network grows steadily each day in recent years. The innovation in the manufacturing industry also the reason for the production of different devices that uses various technologies to make a possible connection between the devices. Even though the Internet of Things has been developing and demonstrating its potential in recent years, its security when connected to the internet is in doubt. Blockchain is a disruptive technology that provides security to their network without tampering with the data in the network. Researchers and experts have recommended using the blockchain to address security vulnerabilities in the Internet of Things. In this paper, we have analyzed some of the issues which are occurring while integrating blockchain into the Internet of things. The major issues were discussed and which will be helpful to move towards the research direction to solve those problems. 2022 IEEE. -
Security Aspects for Mutation Testing in Mobile Applications
Due to the increase in the number of Android Platform Devices, there are more and more applications being developed across various domains. It is interesting to see the involvement of bugs/crashes even in the deployed applications even though it has been through various test phases. Unit tests are essential in a well-trusted testing environment; however, it does not guarantee that the range of test caries every component of the application. This writes up discusses the overview of mutation testing method concerning Android Applications. Even though mutation testing is found out to be very effective in other applications, it is not that easy to implement the same for an Android Developed Application because of additional resources it would hold. Further, various measures for mutation testing are discussed with types of mutant operators, tools etc. The current studies of mutation analysis mainly focus on testing all the functionalities irrespective of the resource usage. However, the target of the future mutation tests must be also to evaluate the efficiency of the applications under the same test cases. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Security and privacy issues in existing biometric systems and solutions
[No abstract available] -
Security and Privacy in Internet of Things (IoT) Environments
Although the proliferation of IoT devices has led to unparalleled ease of use and accessibility, it has also raised serious privacy and safety issues. Using a systematic approach that incorporates security and privacy modelling, data analysis, and empirical trials, this study provides a deep dive into the topic of IoT security and privacy. Our results show how crucial it is to take precautions against 'Information Disclosure' by using strong encryption and authorization protocols. The need to protect against 'Unencrypted Data' vulnerabilities is further emphasized by vulnerability analysis. Encryption (AES-256) and other access control rules fare very well in the assessment of security systems. Furthermore, 'Homomorphic Encryption' is identified as a potential strategy to protecting user privacy while retaining data usefulness based on our review of privacy preservation strategies. A more secure and privacyaware IoT environment may be fostered thanks to the findings of this study, which have ramifications for the industry, government, consumers, and academics. Addressing the ever-evolving security and privacy issues in the IoT will need a future focus on cutting-edge security mechanisms, privacy-preserving technology, regulatory compliance, user-centric design, multidisciplinary cooperation, and threat intelligence sharing. 2024 IEEE. -
Security and privacy aspects in intelligence systems through blockchain and explainable AI
Explainable AI (XAI) is a method of creating artificial intelligence (AI) systems that are transparent and understandable to humans. By allowing people to understand how the system arrived at its conclusions or suggestions, XAI systems strive to make AI more accountable, trustworthy, and ethical. Responsibility, trust, ethics, regulation, and innovation are some of the societal ramifications of XAI. By making AI systems more transparent, XAI fosters accountability. This means that consumers will be able to understand how the system made its decisions and hold it accountable if something goes wrong. By making the decision-making process more transparent, XAI fosters trust between people and AI systems. This boosts user trust in the system and encourages wider adoption of AI technologies. It also contributes to the ethical design of AI systems by making the decision-making process public in order to uncover and mitigate biases and other ethical issues that may occur in AI systems. It aids regulators and policymakers in understanding and regulating AI systems. XAI gives insight into how AI systems operate, which can assist regulators in developing laws that promote ethical and responsible AI use. Because XAI can help developers better and innovate new systems by making it easier for them to design new AI systems and by providing insights into how AI systems work. The proposed chapter will focus on important aspects of algorithmic bias and changing notions of privacy in XAI, which will necessitate the need for AI systems that can adapt accountability, trust, ethics, and compliance with regulations, as well as produce better innovation that can benefit humanity. More openness, greater control over personal data, new types of data privacy, and newer privacy networks are all required. To address algorithmic bias in XAI, it is critical to build the system so that it is aware of the possibility of bias and actively mitigates it. This can involve employing diverse and representative data, inspecting the system for unwanted features, offering detailed explanations, and incorporating a wide range of stakeholders in the system's development and deployment. The envisaged report provides a framework that combines XAI and blockchain to provide a secure and transparent way to store and track the provenance of data used by XAI systems, validate the performance of AI models stored on the blockchain on decentralized systems so that the models are stored and executed on a distributed network of nodes rather than a centralized server, and create a token-based economy that encourages data sharing and AI development. Tokens can be used to compensate individuals and organizations who contribute data or algorithms to the blockchain or who employ AI models stored on the blockchain. Overall, the combination of XAI and blockchain can lead to more trustworthy, transparent, and decentralized AI systems. This approach can have a significant impact on various industries such as finance, healthcare, and supply chain management by increasing efficiency, reducing costs, and improving data privacy and security. 2024 Elsevier Inc. All rights reserved. -
Security analysis in multi-tenant cloud computing healthcare system
Cloud Computing is an innovation in the field of Information Technology and in healthcare system because of the deployment models which services as profitability for the tenants. Cloud Computing is cost-effective, flexible and a delivery platform which provides business and services over internet. Multi-tenancy with its hardware sharing and high degree of configurability is utilized in cloud computing health care system even though many health care organizations are unwilling to adapt due to infrastructure and security shortcoming. In order to store the sensitive health care data cloud service providers should include promising security features where both the trusted and untrusted parties should be addressed in it. This paper addresses the security requirements and security issues in multi-tenant healthcare system, a frame is proposed for analyzing the security issues based on the available requirements and possible counter measures been suggested. The security concerns are analyzed by trust, confidentiality, integrity, audit and compliances and furthermore insight for the security is provided in multi cloud with possible security recommended for healthcare system. IAEME Publication. -
Security Analysis for a Revocable Multi-Authority ABE-Attribute-Based Mechanism
Due to the tremendous increase in data, groups or even organizations are storing data with third-party providers to solve storage problems. Ciphertext policy attribute based encryption helps to outsource data, which means encrypt the data at the data owners end and uploading it to third-party storage with some access policy. In normal Identity-based encryption, if a data owner wants to send information to a data user, it will be sent with some identity of the data user, such as mail id, so that only that particular user can read the message. The main problem is that the data owner should know each users identity. For instance, in some organizations where a data owner wants to send a message to a group of people with an identical designation, it can be sent with the help of the users attribute using attribute-based encryption. Here, the data owner does not need to know the specific details of each user; instead, with the help of attributes and the provided access policy, they can access this message. This research mainly focuses on three aspects of CP-ABE: access policy, number of attribute authorities, and revocation. When it comes to access policy, the currently existing access policies are not secure due to their linearity in nature because shares are always calculated using the same linear equation. So, for this problem, this work has developed a non-linear SS-secret-sharing model that increases the confidentiality of the model. 2024 Seventh Sense Research Group. -
Securing Trust in the Connected World: Exploring IoT Security for Privacy in Connected Environments
This abstract delves into IoT security measures to ensure privacy in connected environments. It examines encryption, authentication, access control, and data privacy techniques. Key considerations include end-to-end security, vulnerability mitigation, regulatory compliance, and user trust. By addressing these challenges, trust can be established in the connected world, enabling the widespread adoption of IoT technologies while safeguarding user privacy. 2024 IEEE.