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Landslide Monitoring Using IoT System with Cloud Platform

Landslide Monitoring Using IoT System with Cloud Platform

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Pradeep Singh Rawat Prateek Kumar Soni Aryan Goel Gurmeet Kaur Computer Science & Engg. Department DIT University Dehradun India Computer Engineering & Application GLA University Mathura India

The natural disasters poses the losses in the disaster prone area. Landslides pose great socio-economic threats to regions which geography of the location favors it. The hill states of the India are more prone to the landslides due to the heavy rain fall other environmental challenges. The researchers found that ninety-nine percent of landslides induced due to heavy rain falls. The severity of disasters especially landslides is gradually increasing day by day in scale and frequency, resulting in widespread loss of human lives and infrastructure of the nation. The detection, monitoring and mitigation of the landslides in hill states is the major concern. The mitigation and monitoring of the effects of landslides can be performed using technology integration. This article presented an innovative landslide monitoring system. It is based on the IoT protocols based monitoring system consisting of cloud platform which provides instant alerts of upcoming landslides on the basis of real time data set collected inside the IoT cloud platform. As part of this monitoring system, landslide data is sent directly to the civil protection department and notified to the local population for the awareness and future precautionary safety measures. The results and discussions section shows the real time data visualization of the landslide inducting parameters, including metrological, geophysical and hydrological parameters.

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Securing Cloud-Based Systems: DDoS Attack Mitigation using Hypervisor-Intrusion Detection Approach

Securing Cloud-Based Systems: DDoS Attack Mitigation using H...

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6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Kumar, Surendra Kumar, Pawan Kumar, Jaydip Singh, Virendra Yadav, Arun Singh Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science and Applications Coer University Roorkee India Department of Computer Application Integral University Lucknow India Department of Computer Science University of Lucknow Uttar Pradesh Lucknow India

The principle of virtualization and cloud-based technology has facilitated various industries in establishing expansive cloud environments. The Cloud is vulnerable to emerging threats and breaches due to the nature of its networks. Creating an intrusion system that addresses security concerns in cloud networks is crucial. This paper examines specific security weaknesses associated with significant threats, such as Distributed Denial-of-Service (DDoS) assaults, which jeopardize both data security and service availability in Cloud systems. To address these concerns, a Hypervisor Controller-based detection system has been developed for the identification of DDoS attacks. The Hypervisor Controller employs Fuzzy Time Series Analysis and Expectation Maximization methods in a dual-phase process. In the preliminary phase, resource requests are categorized according to their compliance with the Service Level Agreement (SLA). Requests that contravene the Service Level Agreement (SLA) are promptly identified as potential malware and denied from system access, thereby preserving the system's security and stability. In the second step, non-violation requests undergo rigorous examination utilizing a Fuzzy Time Series model, simulated by the Expectation Maximization method. This method proficiently manages large datasets, significantly enhancing detection accuracy. The proposed Hypervisor Controller has demonstrated effectiveness, robustness, and immediacy in detecting DDoS attacks across federated systems and cloud environments, as evidenced by experimental and performance evaluations. This study aims to enhance the security of Cloud-integrated systems by formulating comprehensive and effective solutions to address emerging threats. © 2025 The Author(s). Published by Elsevier B.V.

关键词： computer viruses

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Analyze and Compare the Public Cloud Provider Pricing Model and the Impact on Corporate Financial 7th

Analyze and Compare the Public Cloud Provider Pricing Model ...

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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Singh, Jaideep Pal, Souvik Sarkar, Bikramjit Selvi, H. Adhikari, Saurabh Madhumathi, K. Akila, D. Liverpool Business School Liverpool John Moores University Liverpool United Kingdom Department of Management Information Systems Saveetha College of Liberal Arts And Sciences Saveetha Institute of Medical And Technical Sciences Chennai India Kolkata India Department of Computer Science and Engineering JIS College of Engineering Kalyani India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed University Chennai India School of Engineering Swami Vivekananda University Kolkata India Department of Computer Application Anna Adarsh College for Women Chennai India Department of Computer Applications Saveetha College of Liberal Arts and Sciences SIMATS Deemed University Chennai India

ISBN: (纸本)9789819995615

In order to better understand the relative costs of IaaS, PaaS, and SaaS, it is recommended that this study collect data on these three categories of cloud computing services. The purpose of this research is to analyze how much money is saved or lost while adopting cloud services for different-sized enterprises. Its purpose is to identify and quantify outcomes that are beneficial to society, whether they be monetary or otherwise. Additionally, we want to provide a complete picture of cloud services' economic effect by itemizing their benefits and drawbacks. The collected data is stored in a secure location and treated ethically to prevent unauthorized access. Then, any conflicts interesting are investigated thoroughly and resolved. The researcher may be certain that the study is being conducted in a responsible and moral manner if they follow these criteria. The research summed up with the conclusion that every device is capable of running any SaaS application. It could be a smartphone, laptop, desktop computer, or tablet. In this manner, businesses can better protect their data, and employees may take advantage of the flexibility of accessing services from any device so long as they have their secure line key. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Web services

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The Role of Machine Learning in Optimizing Industrial Communication Protocols

The Role of Machine Learning in Optimizing Industrial Commun...

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Advances in Computing Research on science engineering and Technology (ACROSET), International Conference on

作者： Vikas Kumar Kharbas Mahesh TR Satish Upadhyay Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer Science Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore India Department of uGDX ATLAS SkillTech University Mumbai India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Due to the complexity and urgency of information exchange in modern companies, optimizing communication protocols in the workplace is essential. Using methods from machine learning, this research takes a fresh tack toward optimizing the performance of communication protocols. The suggested approach integrates Reinforcement Learning (RL), Convolutional Neural Networks (CNNs), and Genetic Algorithms (GAs) to provide a self-optimizing system with dynamic behavior. The RL feature facilitates this protocol's flexibility in the face of dynamic network circumstances by providing rapid feedback. CNNs are used for real-time anomaly detection to improve safety and dependability. GAs methodically explores combinations of protocol parameters to determine the optimal collection that matches specified performance criteria. The recommended technique was compared to the usual methodology using a variety of efficiency and reliability measures. The findings demonstrate that the proposed approach frequently outperforms the state-of-the-art methods in a broad range of commercial settings with regards to efficiency, dependability, and adaptability. Understanding how to enhance industrial communication protocols is crucial in the age of Industry 4.0 and the IIoT. The recommended solution offers an autonomous, adaptable, and secure tool for boosting communication in dynamic production situations.

关键词： Protocols Industrial communication Reinforcement learning Production Real-time systems Reliability Vehicle dynamics Optimization Genetic algorithms Industrial Internet of Things

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Empirical Analysis of Prediction of Crime and Forecasting With IGWO-SVM Approach 2

Empirical Analysis of Prediction of Crime and Forecasting Wi...

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2nd International Conference on Edge Computing and applications, ICECAA 2023

作者： Vasuki, R. Ganvir, Neha Sharma, Rohita Kumar, K Senthil Krishna Kumari, Svhn Singh, Kamlesh Mannar Thirumalai Naicker College Department of Computer Application Tamilnadu Madurai India Kj College of Engineering and Management Maharashtra Pune India Lovely Professional University Department of Fine Arts Punjab India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of and Communications Tamil Nadu Chennai India Telangana India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350347579

In order to understand the nuances of the crime data from recent years, in-depth analysis is necessary. Involved parties in law enforcement will benefit from this procedure in terms of making arrests and guiding efforts to reduce crime. Strategically or tactically, it benefits from having a better understanding of where, when, and how crimes will occur in the future. However, given the rising incidence of crime in the modern world, developing an effective method for predicting future criminal behavior is a difficult issue. As a result, it is crucial to develop a strategy for predicting criminal activity in order to prevent further crimes and lower crime rates. Researchers are currently attempting to predict criminal behavior based on a variety of factors. SVM, CNN and ANN are just few of the prediction approaches that can be used to assess a model's accuracy. Although their studies have yielded encouraging findings, there are still major blind spots that preclude an accurate prediction of crime hubs. There have been many earlier publications of academic papers on this subject. So, the results are compiled and conducted a comprehensive assessment of all of them in this report. Proposed goal is to catalog the various forms the crime prediction method has taken and the ways in which it might be improved in the future. Preprocessing, feature extraction, and model training are the three main components of the proposed method. Preprocessing includes activities including data cleansing, integration, transformation, reduction, and discretization. It uses principal component analysis to glean features. The model is trained with IGWO-SVM. When compared to two existing models, the proposed model performs exceptionally well. © 2023 IEEE.

关键词： Support vector machines

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Re-Cognition I: An Inspection of Learner's Cognitive Well-Being, Pursuant to Contemporary Period, Facilitates the Academic Life Cycle

Re-Cognition I: An Inspection of Learner's Cognitive Well-Be...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Souvik Ganguly Kailash Pati Mandal Debasis Guha Dept. of Computer Application Dr. B.C. Roy Engineering College Durgapur India Dept. of Computer Science and Engineering National Institute of Technology Durgapur India

Recommender systems enhance the user experience by integrating factual and behavioral practices, enhancing learning strategies, and granting academic opportunities based on learners subjective interests. Cognitive intelligence, a learner's capability to interpret and synthesize cognitive abilities, is crucial to assemble effective recommendations. However, a student encounters diverse adversities in his or her academic life, whether it is his or her academic pacers or outward circumstances. As a result, a learner consequently experienced severe academic setbacks. Thus, this paper proposes a novel approach to identify cognitive deficits of learners in a school or college before recommending any scholastic content or pertaining in any academic activity. We have employed a modified Bi-directional Gated Recurrent Unit (Bi-GRU) method in conjunction with Shuffled Frog Leaping Algorithm (SFLA). Our approach overcomes some of the shortcomings of conventional Bi-GRU method in choosing the right scaling parameters during training. Bi-GRU method is opted for detecting the cognitive deficits of a learner and SFLA is applied over Bi-GRU in order to hyper-tune the parameters. The proposed method is compared with a few cutting-edge methods, and the experimental result outperforms the other methods in training, validation, and testing accuracies, achieving 99.34%, 98.98%, and 99.19% respectively.

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A Robust DL Approach for Detection of Invasive Ductal Carcinoma in Whole Slide Images using DenseNet169

A Robust DL Approach for Detection of Invasive Ductal Carcin...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Menaka, C. Sharma, Sunil Naval, Preeti Brar, Khushmeen Anand, Rohit Alabdeli, Haider Pant, Shivani Department Of Computer Science And Information Technology Bangalore India Vivekananda Global University Department Of Computer Science & Application Jaipur India Maharishi University Of Information Technology Maharishi School Of Engineering & Technology Uttar Pradesh India Chandigarh University Department Of Electronics And Communication Engineering Punjab Mohali India G.B Pant Okhla-1 Campus Department Of Ece New Delhi India The Islamic University Najaf Iraq Uttaranchal University Uttaranchal Institute Of Technology Uttarakhand India

ISBN: (纸本)9798350366846

The present research introduces a deep learning method for automatically recognizing and evaluating tissue areas that correspond to invasive ductal carcinoma (IDC) in whole slide images (WSI) of breast cancer (BCa). Learn-from-data techniques which utilize computational simulations of the learning process are called deep learning approaches. It has been demonstrated that these methodologies outperform conventional methods in resolving the challenging issues in a number of domains, particularly object and speech recognition. The main reason why making an invasive breast cancer evaluation is an exhausting and laborious process is that, it necessitates a pathologist to scan a huge number of benign regions with the aim to determine the spots that are malignant. Effectively characterizing IDC in WSI is a prerequisite for evaluating the severity of tumors and predicting patient prognosis. In order to support diagnosis, the DL approach in this study draws out many convolutional neural networks (CNNs) for perceptible semantic examination of tumor locations. A WSI database of 162 patients with IDC diagnoses is exploited to assess the technique, and 170 slides were chosen for training. The objective of the experimental analysis was to assess the accuracy of the classifier in identifying IDC tissue areas in WSI. A fusion of optimization techniques namely Best First and Evolutionary Search has been employed for feature reduction and improvement. When using DenseNet169, our approach produced the highest quantitative outcomes for the computerized identification of IDC sections in WSI with regard to accuracy 87.46% in comparison with an approach using EfficientNetB0 and ML classifiers for invasive tumor categorization employing Random Forest, BayesNet, SVM, Naïve Bayes and Decision Tree with accuracy of 80.65%. © 2024 IEEE.

关键词： Diagnosis

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SSHTDNS: A Blockchain-Based Secure, Scalable, and High-Throughput Domain Name System

SSHTDNS: A Blockchain-Based Secure, Scalable, and High-Throu...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Baldev Singh Kavita Department of Computer Science Engineering Vivekananda Global University Jaipur India Department of Master of Computer Application Chandigarh School of Business

The Internet's crucial domain name system is in charge of translating domain names into IP addresses. However, because of its dispersed structure with a hub, it is subject to significant security concerns. Since the creation of Bitcoin, blockchain has drawn a lot of attention owing to its decentralized, trustworthy, and secure features. In this article, we suggest SSHTDNS, a brand-brand-new domain name system built on the blockchain. SSHTDNS selects the consortium chain form to ensure the management of Top-Level Domain names, and these consortium nodes use linkable ring signature technology for identity anonymity and vote fairness. Shading protocol is used in multi-chain structures to increase the system's scalability and throughput. A domain name system that combines these methods can be safe, scalable, and high-throughput.

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Elevating Cybersecurity in Defence: A Collaborative and Technologically Driven Approach

Elevating Cybersecurity in Defence: A Collaborative and Tech...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Syed Anas Ansar Shruti Aggarwal Swati Arya Mohd Asim Sayeed Nupur Soni Prabhash Chandra Pathak School of Computer Application Babu Banarasi Das University Lucknow India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab India

As the world enters the 21st century, the imperative of safeguarding cybersecurity has intensified. Considering the expanding dependence on information technology (IT) systems for a broad spectrum of military operations, the stakes have never been higher for thwarting successful cyberattacks. Conventional security protocols have become increasingly precarious in the face of evolving cyber threats. This necessitates the adoption of avant-garde and nascent technologies to bolster the military's cybersecurity stance. The paper delves into an array of incipient technologies that proffers the prospect of enhancing cybersecurity in the realm of defence. These include artificial intelligence (AI), machine learning (ML), blockchain, and quantum computing. AI can be leveraged to automate numerous cybersecurity functions, such as threat detection and response. ML can augment the precision of threat detection and response systems. Blockchain, a distributed ledger technology, can safeguard data and transactions. Quantum computing, a nascent technology, harbours the potential to revolutionize computation. It could be utilized to breach present-day encryption standards, thereby rendering data vulnerable to attackers. Moreover, the paper appraises the exigencies and possibilities in defence cyber security and scrutinizes the policy and practice ramifications of these emergent technologies and best practices.

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Empowering Supply Chain Transparency and Risk Management through Hybrid CNN and BiGRU Approach 1

Empowering Supply Chain Transparency and Risk Management thr...

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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Guhan, K. Lakshmi, S. Verma, Virendra Kumar Sai, M K Satya Sharma, Amit Kumar, B. Suresh Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India Bharati Vidyapeeth Management Studies and Research Department of Management Business Administration Navi Mumbai India Symbiosis Institute of Business Management Bengaluru India Vignana Bharathi Institute of Technology Department of Mechanical Engineering Hyderabad India Lovely Professional University School of Computer Application Phagwara India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronic Engineering Telangana Hyderabad India

ISBN: (纸本)9798350352931

Everything from needs to luxuries has been impacted by the COVID-19 pandemic in the manufacturing sector, and that includes the automotive industry. Thus, there is an immediate need for research into the automotive supply chain's sustainability, transparency, and competency development practices. For the automotive supply chains to successfully apply the Transparency for Sustainability (TfS) architecture in the long run, it also specifies a set of skills that are necessary. The three steps that comprise this proposed method are preprocessing, feature selection, and model training. The data had to be preprocessed in order for it to be ready for analysis. The feature selection procedure is comprised of the LDA topic model, term frequency, and correlation analysis. A GRU-DT was utilized for the model's training. The suggested method outperforms GRU and DT with an average accuracy of 96.29%. © 2024 IEEE.

关键词： Automotive industry

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