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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Kumar, Narendra Aeron, Anurag Dawar, Ishaan Kumar, Santosh Department of Computer Science and Engineering Amity University Jharkhand Ranchi India School of Computing DIT University Dehradun Dehradun India Computer science and Information Technology Siksha 'O' Anusandhan University Bhubaneshwar India

ISBN: (纸本)9798350383201

The Internet of Things (IoT) has greatly increased the possibility for developing intelligent connections and applications in many facets of daily life. Traditional security solutions frequently fail to solve security issues in cloud-based IoT systems, proving ineffective and insufficient. Software Defined Networking (SDN), which effectively detects and monitors network security vulnerabilities thanks to its programmable characteristics, presents a viable solution to these problems. In order to strengthen computer systems and fend off threats to network security, machine learning (ML) techniques have recently been included into SDN-Network Intrusion Detection Systems (NIDS). Deep learning (DL), one of these cutting-edge ML techniques, has become more popular in the SDN environment. For SDN-based cloud IoT networks, this paper design an efficient path selection system in this study utilizing DL approaches. The strategy comprises employing a Recurrent Neural Network (RNN) to identify threats and a selection mechanism to pick the best route for data delivery. This study performed tests on two datasets, KDD and UNSWNB15, and the results show that the suggested RNN outperformed existing DL methods by achieving an amazing accuracy ranging from 91% to 95% on both datasets. © 2023 IEEE.

关键词： Internet of things

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Graph-Based Generation and Validation of Use Case Diagrams

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Procedia Computer science 2025年 259卷 1356-1365页

作者： Reshma P Nair M G Thushara K Somasundaram Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amrita School of Computing Amritapuri Kerala India Department of Computer Science and Applications Amrita Vishwa Vidyapeetham Amrita School of Computing Amritapuri Kerala India Department of Mathematics Amrita School of Physical Sciences Amrita Vishwa Vidyapeetham Coimbatore India

As we know in today’s modern software engineering, use case diagrams for representing the functionalities of the system act significantly in mediating the gap between the stakeholder and the developer. Some of the earlier techniques used in constructing such diagrams can be problematic when these diagrams are to be employed in large systems where there are many actors and activities. That is why every limitation outlined above might be resolved further with the help of NLP and developments in graphs theory. The proposed method makes improvements by reducing the human interference; it can handle increase in system size and the number of actors and their interactions; it also provides highly accurate shortest-path identification through use of validation methods. In addition, shortest-path algorithms are incorporated to improve the validation exercise, thereby minimizing errors common in presenting the system functionalities. Hence, the results indicate that the proposed method is more effective not only for generating use case diagrams with increased precision and scalability but also for constructing the necessary framework for checking system interactions’ correctness. This work provides a foundation for future studies aimed at enhancing the automation of the use case diagram especially for complex systems.

关键词： NLP Graph Theory Use case Diagrams Automatic Generation Shortest-Path Algorithms

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Security Threats and Challenges in Public Cloud Storage

Security Threats and Challenges in Public Cloud Storage

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2021 International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2021

作者： Nagarajan, G Sampath Kumar, K. Galgotis University School of Computing Science and Engineering India

ISBN: (纸本)9781728177410

Cloud computing is a modern way for many businesses to offer computing tools and services that have transformed their business at a low cost. But the main concern is protection and privacy in cloud computing using a security program, encrypted file systems, and data loss software the problem and threats decreased. But the safety risks still exist in current days. Many cloud computing organizations and consumers also do not trust cloud providers to store their confidential data in public cloud storage because of the scarcity of accuracy in the protection of information sharing. This paper focuses primarily on security risks and problems that different cloud platforms face (Education, Enterprise, and Healthcare). © 2021 IEEE.

关键词： Cloud storage

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Block Broyden's Methods for Solving Nonlinear Equations 37

Block Broyden's Methods for Solving Nonlinear Equations

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Liu, Chengchang Chen, Cheng Luo, Luo Lui, John C.S. Department of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong Shanghai Key Laboratory of Trustworthy Computing East China Normal University China School of Data Science Fudan University China

ISBN: (纸本)9781713899921

This paper studies quasi-Newton methods for solving nonlinear equations. We propose block variants of both good and bad Broyden's methods, which enjoy explicit local superlinear convergence rates. Our block good Broyden's method has a faster condition-number-free convergence rate than existing Broyden's methods because it takes the advantage of multiple rank modification on Jacobian estimator. On the other hand, our block bad Broyden's method directly estimates the inverse of the Jacobian provably, which reduces the computational cost of the iteration. Our theoretical results provide some new insights on why good Broyden's method outperforms bad Broyden's method in most of the cases. The empirical results also demonstrate the superiority of our methods and validate our theoretical analysis. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Nonlinear equations

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Application of Machine Learning Predicting Injuries in Traffic Accidents through the Application of Random Forest

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Recent Patents on engineering 2025年第2期19卷 108-121页

作者： Singh, Veer Bhadra Pratap Hemamalini, V. Muttipati, Appala Srinuvasu Raju, S. S. S. V. Gopala Shatil, Abu Hena Md Sharma, Abhishek Department of Cyber Security School of CSIT Symbiosis Skills and Professional University Kiwale Pune India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Bangalore India Department of Civil Engineering Rajiv Gandhi University of Knowledge Technologies Nuzvid Campus Andhra Pradesh 521202 India Dhaka Bangladesh Shri Vaishnav Vidyapeeth Vishwavidyalaya Madhya Pradesh Indore India

Background: The objective of this work is to analyze and predict the harmfulness in traffic accidents. Method: Several Random Forest statistical models are created, in which the predictable variable (response/ output variable) is the harmfulness of the accident, while the input variables are the various characteristics of the accident. In addition, these generated models will allow estimating the influence or importance of each of the factors studied (input variables) concerning the harmfulness of road accidents so that it is possible to know in which aspects it is more profitable to work with the objective of reducing mortality from traffic accidents [1]. Results: In this regard, the predictive algorithm has an out-of-bag error of 26.55% and an overall accuracy of 74.1%. Meanwhile, the local accuracy of the mildly wounded class is 66.1% compared to 81.4% of the dead and severely wounded class, which, as mentioned, has higher prediction reliability. Conclusion: Finally, it is worth noting the enormous usefulness of the Random Forest machine learning technique, which provides very useful information for possible research or studies that may be carried out. In the specific case of this patent work, through the use of the R programming language, which in turn presents a wide range of freely accessible utilities and functions with which it may be interesting working, it has generated results of great value for this area of activity, important to society as road safety. © 2025 Bentham science Publishers.

关键词： Highway accidents

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LoRaWAN Enabled Wildland Safety Fire Alert System 14

LoRaWAN Enabled Wildland Safety Fire Alert System

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Madan, Karan Gautam, Devraj Bhatia, Sandeep Kashyap, Sidhant Chanchal Priyanka Dr. Akhilesh das Gupta Institute of Technology and Management Electronics and Communication Engineering Department New Delhi India Galgotias University School of Computing Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350335095

The Interconnection via Internet is ubiquitous and it is significant to everyone's lives in the area of safety. Forest fires are a result of human intervention, lightning, and global warming and have an impact on both flora and animals. One of the techniques is to use a forest fire alert system. The applications cover alerting systems, intelligent monitoring, and sensing. A LoRaWAN-based IoT intelligent alerting system that is eco-friendly, affordable, compatible, uses little power and has a substantial range is the goal. This paper goals to offer basic structure with the assistance of LoRaWAN and the way this device might be useful over a number of the structures currently used. This paper aims to highlights the importance of LoRAWAN through our proposed system using specific set of sensors to detect fire. The strategies for detecting wildfires are carefully examined in this paper. © 2023 IEEE.

关键词： Fires

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Development of cybersecurity framework for FinTech innovations: Bahrain as a case study

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International Cybersecurity Law Review 2024年第4期5卷 501-532页

作者： AlBenJasim, Salah Takruri, Haifa Al-Zaidi, Rabab Dargahi, Tooska School of Science Engineering & Environment University of Salford Salford UK Computing and Mathematics Department Manchester Metropolitan University Manchester UK

FinTech is the term used to refer to financial and technology convergence space solutions. It usually refers to new innovations that conduct or connect with financial services via the internet, smart devices, software applications, or cloud services and encompasses anything from mobile banking to cryptocurrency applications. Despite the advantages of FinTech, cybercriminals seized the opportunity to exploit vulnerabilities in FinTech systems. Phishing attacks, ransomware, and data breaches have become more prevalent, targeting individuals and FinTech institutions. Bahrain, which is not different from the rest of the world, was impacted by such cyber threats. Thus, FinTech companies have had to strengthen their cybersecurity countermeasures and protocols to combat these threats. Existing countermeasures in the literature primarily focus on general cybersecurity practices and frameworks, with limited attention given to the specific needs of the FinTech industry. Hence, there is a notable gap in the literature regarding a focused cybersecurity framework that caters to the unique requirements of FinTech innovations, especially in Bahrain. To bridge this gap, this research addresses the problem by conducting an extensive review of existing cybersecurity challenges, common practices, and cybersecurity standards and through in-depth research interviews with executives, experts, and other FinTech business stakeholders. Leveraging this knowledge, this research proposed an adaptable framework that addresses the risks and vulnerabilities faced by FinTech innovations in Bahrain. Through panel discussions and Delphi sessions, industry experts evaluated the framework’s practical feasibility, ability to address specific risks, and compatibility with the existing FinTech regulatory landscape. The results demonstrate a high acceptance of the developed framework and highlight the framework’s potential to enhance cybersecurity resilience significantly. Moreover, the exp

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Diagnosis of Cardiac Diseases Using Ranked Parameters for Machine Learning 23

Diagnosis of Cardiac Diseases Using Ranked Parameters for Ma...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Shashi Kant Tiwari Shweta Sinha Karamjit Kaur Sekhon Ram Sewak Singh Amity School of Engineering & Technology AMITY UNIVERSITY India Amity School of Engineering & Technology Amity University India 4School of Electrical and Computing Engineering Adama Science and Technology University Adama 4School of Electrical and Computing Engineering Adama Science and Technology University Adama Ethopia

ISBN: (纸本)9798400709418

Abstract: This paper introduces an innovative approach to evaluate the classification performance of parameters derived from diverse spectral and nonlinear investigation methods in the analysis of cardiac heart disease using ranking techniques. The objective of this study is to identify the most suitable methods for application in a clinical setting. Ranking methods are employed to assign ranks to features extracted from heart rate variability (HRV) data by spectral and chaos investigation methods, organizing them based on their clinical significance. Initially, 30 and 10 different features are extracted from HRV time-series signals using popular HRV analysis methods, including spectral and nonlinear investigation techniques. Among these, 5 spectral features are derived from the auto-regression (AR) model, 15 from multiscale Wavelet packet (MSWP) decomposition, and 10 from higher-order spectra (HOS). The top ten features from each method are selected and ranked using Fisher score, Wilcoxon, receiver operating characteristics (ROC), entropy, and Bhattacharya space methods. Numerical results demonstrate that features at the third level of MSWP decomposition exhibit the lowest p-value (<<0.0001). Given that features with the lowest p-value possess the highest discrimination ability, the third-level MSWP features outperform other features extracted by AR and HOS spectral methods in HRV dataset analysis. Furthermore, features such as CD, DFA:α₂, ApEn, SaEn, and SD1/SD2, derived from chaos investigation methods, consistently display the lowest p-values compared to all considered features. Consequently, these features are identified as optimal choices for HRV analysis of datasets.

关键词： Heart diseases

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Face Recognition CCTV for Automated Attendance Tracking

Face Recognition CCTV for Automated Attendance Tracking

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IEEE Students' Conference on Electrical, Electronics and Computer science (SCEECS)

作者： Sai Sindhu Kola Harsha Kenchi P. Rama V. Angayarkanni J. Kalaivani S. Sivakumar Computing Technologies College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies College of Engineering and TechnologyFaculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Computing Technologies College of Engineering and TechnologyFaculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur TN India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai Department of Computing Technologies Faculty of Engineering and Technology SRMIST Kattankulathur

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

This study investigates the design and execution of an automated attendance tracking system using facial recognition CCTV based . Facial recognition technology and CCTV cameras are integrated in this system to provide a seamless, frictionless way to track and record attendance in real time. In addition to boosting attendance record accuracy, the approach reduces the administrative burden associated with using traditional methods. The suggested system is designed to recognise people when they enter or leave a monitored area by leveraging developments in computer vision. It accomplishes this automatically and requires no interaction from humans. Furthermore, the system's use of the Internet of Things makes it flexible and scalable to a variety of business situations, including offices, schools, and large-scale events. This is made feasible via real-time data synchronising and reporting.

关键词： Computer science Electric potential Computer vision Accuracy Face recognition Cameras Real-time systems Internet of Things Monitoring Business

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Advancing Vascular Dementia Diagnosis Through Salp Swarm Optimized Convolutional Neural Network 8

Advancing Vascular Dementia Diagnosis Through Salp Swarm Opt...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Mathkunti, Nivedita Manohara Ananthanagu, U. Abdulpur, Vidya N. Mangaiyarkarasi, D. Anitha, S. Alliance School of Advanced Computing Alliance Univerisity Department of Computer Science & Engineering Karnataka Bengaluru India Dr.D.Y Patil Pratishthan's College of Engineering Department of Electrical Engineering Maharastra Kolhapur India Institute of Science and Technology Avadi India Department of Artificial Intelligence & Machine Learning Karnataka Bengaluru562106 India

ISBN: (纸本)9798331505462

Vascular Dementia (VD) is a form of cognitive impairment seen in individuals, restricting from problems with blood vessels supplying the brain. These issues lead to strokes or reduced blood flow and gradually lead to the development of vascular dementia. Since it tends to progress slowly, diagnosis often comes after significant brain damage making prevention is crucial. Early identification of risk factors are vital to mitigate the risk of irreversible cognitive decline associated with vascular dementia. Accordingly, this research proposes an optimized neural network model for predicting vascular dementia at its earlier to improve the quality of life. The initial stage in this research involves preprocessing, where the input data is cleaned and normalized. These steps ensure that the vascular dementia dataset is cleaned of inconsistencies and prepared with normalized features for accurate analysis and modeling. Following preprocessing, relevant data features are selected using the chi-square approach, making the classification task easier. Finally, classification is performed utilizing a Convolutional Neural Network (CNN) and for fine-tuning of CNN parameters Salp Swarm Optimization (SSO) Algorithm is implemented. The execution of proposed work is processed using Python software and the outcome reveals superior performance in terms of accuracy and precision. Thereby the proposed method provides better performance with an accuracy of 94.5%, contrasted to existing methods in predicting VD. © 2024 IEEE.

关键词： Convolutional neural networks

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