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Security in Social Networks and Media 1

Security in Social Networks and Media

引用

1st IEEE international conference on advances in Electronics, Communication, computing and intelligent Information systems, ICAECIS 2023

作者： Jyothi, A.P. Badiger, Vinayak N Shivakumar, Vinyas Ramaiah University of Applied Sciences Faculty of Engineering and Technology Dept. of Computer Science Karnataka Bengaluru India

ISBN: (纸本)9798350348057

In today's data-driven world, data privacy and security is of utmost importance. Social media platforms have become the main source of data breaches due to the sheer number of users who are exposed to these risks. It is essential to understand the viable risks associated with such breaches in order to ensure that personal data remains secure. In this paper, we have discussed and reviewed the different forms of digital privacy breaches, their risks, possible preventive strategies and responsibilities of Institutions and Governments to handle digital security. © 2023 IEEE.

关键词： data privacy

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Multi-sensor data Fusion and Deep Machine Learning Models-Based Mental Stress Detection System 1

引用

2nd international conference on advances in data-driven computing and intelligent systems, adcis 2023

作者： Gedam, Shruti Paul, Sanchita Jharkhand Mesra Ranchi India

ISBN: (数字)9789819995240

ISBN: (纸本)9789819995233

Stress is a concern in today’s paced society impacting individuals in various aspects of their lives including educational environments. It is crucial to identify and examine students’ stress levels as it offers insights into their well-being academic performance and excellence of life. The goal of this study is to develop a mental stress detection system that utilizes a combination of multiple sensor’s data fusion and deep machine learning (ML) models with the help of three physiological signals named electrocardiogram (ECG), galvanic skin response (GSR) and skin temperature (ST). data is gathered from 200 students with the help of some stressors using a novel Internet of Medical Things (IoMT) device developed using low-cost sensors. Then the data went through pre-processed methods before being analyzed using some techniques of deep ML models like multi-layer perceptron neural networks (MLPNN), recurrent neural networks (RNN), and long short-term memory (LSTM) networks to identify stress. The results show that the RNN and LSTM models regularly outperform the MLPNN model in recognizing mental stress. These models adequately depicted the dynamic character of stress reactions while efficiently capturing temporal dependencies. The accuracy of the RNN and LSTM models was 92.34% and 93.20%, respectively. These models’ improved performance demonstrates their potential in stress detection applications. The combination of multi-sensor data fusion with deep ML algorithms allows for the precise detection of stress-related physiological changes. This has major consequences for student well-being and academic success since it enables personalized stress management tactics and interventions. Student’s mental health and academic experience can be improved by proactive efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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IoT-driven Healthcare: A New Era of Connected Care 2

IoT-Driven Healthcare: A New Era of Connected Care

引用

2nd international conference on advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Chophla, Elisha Sharda, Sanchita Madan, Suman Chopra, Ankita Department of Information Technology Jagan Institute of Management Studies Sec- 5 Rohini Delhi India Department of Information Technology ATLAS SkillTech University Mumbai India

ISBN: (纸本)9798331541217

With science and technologies taking quantum leaps, the Internet of Things (IoT) has come out as a revolutionary force in global connectivity, bridging the physical and digital realms through sensor and actuator interfaces. Even in the healthcare sector, it enhances medical efficiency and patient care. It connects devices and enables intelligent data-driven systems. This study works towards delving into IoT's fundamentals and its deployment in the healthcare domain. It presents the outline of IoT's structure which includes a variety of technologies like sensors, embedded intelligence, RFID, and nanotechnology. It illustrates how these technologies facilitate connectivity and data exchange between interconnected devices. The paper emphasizes the relevance of the Internet of Things in healthcare. This comprises wearable ECG monitoring, applications in home mobile healthcare systems, and fog-computing architecture for preventive healthcare. Altogether, the paper focuses on expanding the importance of IoT in restructuring healthcare and improving the quality of life. © 2024 IEEE.

关键词： Fog computing

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Multivariate data-driven Approach to Identify Reliable Neural Components and Latency in a P300 dataset Using Correlated Component Analysis 1

引用

international conference on advances in data-driven computing and intelligent systems, adcis 2022

作者： Hazarika, Kalpajyoti Gupta, Cota Navin Neural Engineering Lab Department of Bioscience and Bioengineering IIT Guwahati Assam India

ISBN: (数字)9789819909810

ISBN: (纸本)9789819909803

This paper demonstrates the use of correlated component analysis to identify reliable and highly correlated P300 components and its associated latency. Electroencephalogram (EEG) trials from eight channels collected during an oddball experiment were used in this analysis. All the trials were divided into non-target (non-P300) and target (P300) trial cohorts. data-driven correlated component analysis (CorrCA) was applied to both cohorts separately. We observed that first CorrCA components from target trials showed more coherent structure of p300 latency than single electrode (Cz) trials when those were visualized in event-related potential (ERP) image plot. The averaged first CorrCA component across P300 trials cohort showed higher amplitude at P300 latency when compared to non-P300 trials. P300 amplitude of averaged first CorrCA component across all able bodied subjects was 0.37 μ V higher than P300 amplitude of averaged target trials at Cz electrode. Forward model considering eight channels showed lower neural activation in disabled subjects than abled-bodied subjects. Through this work, we show the utility of the CorrCA method for traditional P300 experiments. The presented application-level multivariate framework can be used to obtain critical parameters like latency from event-related potential datasets recorded for various psychiatric conditions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Signal to noise ratio

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Harnessing Insights from Streams: Unlocking Real-Time data Flow with Docker and Cassandra in the Apache Ecosystem 6

Harnessing Insights from Streams: Unlocking Real-Time Data F...

引用

6th IEEE international conference on Recent advances in intelligent Computational systems, RAICS 2024

作者： Oza, Jay Patil, Abhijit Maniyath, Chirag More, Rishi Kambli, Gitesh Maity, Amit K.J. Somaiya Institute of Technology Computer Engineering India

ISBN: (纸本)9798350381689

Real-time data streaming pipelines are immensely valuable in today's data-driven world since they enable continuous data processing and analytics. This research paper provides a comprehensive exploration of the architecture, development, and deployment of an advanced real-time data streaming pipeline. It utilizes Docker for containerization, Apache Kafka for distributed streaming, Apache Spark for dynamic data transformation, and Cassandra for efficient NoSQL storage. The study outlines the intricacies of integrating these technologies, examining the pipeline's components, functionalities, performance metrics, and potential applications. Through this case study, the paper show-cases the efficacy of open-source tools in constructing highly scalable and resilient data streaming pipelines. © 2024 IEEE.

关键词： data streams

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Predictive Risk and Complexity Score Assessment Model for Cloud computing

Predictive Risk and Complexity Score Assessment Model for Cl...

引用

2024 international conference on advances in data Engineering and intelligent computing systems, ADICS 2024

作者： Ayappan, Kavitha Mathana, J.M. Thangakumar, J. Hindustan Institute Of Technology & Science Chennai India

ISBN: (纸本)9798350364828

With the rapid growth in information technology and being called the Digital Era, it is very evident that no one can survive without internet or ICT advancements. The day-to-day life operations and activities are dependent on these technologies. The latest technology trends in the market and industry are computing power, Smart devices, artificial intelligence, Robotic process automation, metaverse, IOT (Internet of things), cloud computing, Edge computing, Block chain and much more in the coming years. When looking at all these aspect and advancements, one common thing is cloud computing and data which must be protected and safeguarded which brings in the need for cyber/cloud security. Hence cloud security challenges have become an omnipresent concern for organizations or industries of any size where it has gone from a small incident to threat landscape. When it comes to data and cyber/ cloud security there are lots of challenges seen to safeguard these data. Towards that it is necessary that everyone must be aware of the latest technological advancements, evolving cyber threats, data as a valuable asset, Human Factor, Regulatory compliance, Cyber resilience. To handle all these challenges, security and risk prediction framework is proposed in this paper. This framework PRCSAM (Predictive Risk and Complexity Score Assessment Model) will consider factors like impact and likelihood of the main risks, threats and attacks that is foreseen in cloud security and the recommendation of the Risk management framework with automatic risk assessment and scoring option catering to Information security and privacy risks. This framework will help management and organizations in making informed decisions on the cyber security strategy as this is a data driven, dynamic & proactive approach to cyber security and its complexity calculation. This paper also discusses on the prediction techniques using Generative AI techniques. © 2024 IEEE.

关键词： Cybersecurity

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6th international conference on Recent advances in intelligent Computational systems, RAICS 2024

6th International Conference on Recent Advances in Intellige...

引用

6th IEEE international conference on Recent advances in intelligent Computational systems, RAICS 2024

ISBN: (纸本)9798350381689

The proceedings contain 119 papers. The topics discussed include: efficient adder designs for realizing addition subset of RISC-V P-SIMD instructions;artificial intelligence driven portfolio management;assessing knee osteoarthritis severity: a deep learning approach with enhanced ResNet152;pulmonary disease multiclassification of chest X-ray images using deep learning techniques;CT image denoising using autoencoder and generative adversarial networks;power quality analyzer for grid connected inverter;ensuring privacy of data in machine learning;high gain buck boost converter with single switch and low voltage stress;load frequency control using adaptive sliding mode control for a two area interconnected power system;and revolutionizing document summarization: examining key influences on the adoption of ChatPDF for enhanced student learning.

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IoT-Enabled Black Box for Driver Behavior Analysis Using Cloud computing

IoT-Enabled Black Box for Driver Behavior Analysis Using Clo...

引用

2024 international conference on advances in data Engineering and intelligent computing systems, ADICS 2024

作者： Jegan, J. Suguna, M. Raja Shobana, M. Azath, H. Murugan, S. Rajmohan, M. School Of Technology The Apollo University Department Of Computer Science And Engineering Andhra Pradesh Chittoor India Rmk College Of Engineering And Technology Department Of Computer Science And Engineering Tamil Nadu Chennai India Srm Institute Of Science And Technology Department Of Networking And Communication Chennai Tamil Nadu Kattankulathur India School Of Computing Science And Engineering Vit Bhopal University Bhopal India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Computer Science And Engineering Tamil Nadu Chennai India Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

The Internet of Things (IoT)-powered black box for advanced driver behavior analysis and vehicle safety is a revolutionary strategy for improving driver behavior and road safety via the use of black boxes powered by the Internet of Things. This project captures realtime data on driving behavior, encompassing characteristics such as speed, acceleration, braking, and adherence to lanes via the seamless integration of IoT technologies into the vehicle's black box. It does this by providing in-depth insights into drivers' behaviors, which encourages adopting safe driving practices thanks to the advanced data analytics it employs. In addition to its behavior monitoring capabilities, the IoT-driven black box enhances the vehicle's safety by including collision detection technologies. If an accident occurs, the system immediately sends an alert, communicating the severity of the collision and the exact GPS locations to the emergency services, which speeds up the process of responding to the incident. This attempt combines cutting-edge Internet of Things capabilities with vehicle safety to develop responsible driving behaviors and, eventually, a safer highway environment for all parties involved. This invention can modify driving norms thanks to its comprehensive approach, highlighting the potential of technology-driven solutions in promoting safer roadways. © 2024 IEEE.

关键词： Vehicle safety

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A Framework for an intelligent Voice-Assisted Language Translation System for Agriculture-Related Queries 1

引用

international conference on advances in data-driven computing and intelligent systems, adcis 2022

作者： Ajawan, Pratijnya Doddamani, Kaushik Karchi, Aryan Desai, Veena Department of ECE KLS Gogte Institute of Technology Karnataka Belagavi590008 India

ISBN: (数字)9789819909810

ISBN: (纸本)9789819909803

Language translation plays a major role in responding to farmers queries in technology-assisted response systems for agriculture. It is imperative that there should exist a system that can handle the queries raised by a farmer in regional language and respond to queries asked with minimal human involvement. The proposed framework accepts the farmers' queries spoken in Kannada language and translates the Kannada query into English query. The translated query obtained in English from the proposed framework is utilized by the response system to retrieve an appropriate query from a sample set chosen from the Kisan Call Centre dataset for Belagavi district which consists of queries and answer pairs asked by the farmers in English language. Further, the corresponding English text answer obtained from the response model is converted to Kannada text answer, which is translated as Kannada voice answer. The Bilingual Evaluation Understudy score is utilized as a metric for analyzing the efficiency of the framework. The BLEU is calculated by comparing a candidate text translation against reference translations. BLEU 1-gram, 2-gram, and 3-gram scores have been analyzed for evaluating the performance of the translation model. Google translator is incorporated in the framework to translate the query from Kannada speech to English text and vice versa. For experimentation, 9 queries corresponding to the most frequently asked categories from around 50,000 KCC queries are considered. The response generated by the system has been compared with 4 reference translations. 83% translation accuracy is obtained by considering the BLEU score for 1-gram. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Agriculture

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BugFinder: Automatic data Extraction Approach for Bug Reports from JIRA-Repositories

BugFinder: Automatic Data Extraction Approach for Bug Repor...

引用

international conference on advances in data-driven computing and intelligent systems, adcis 2022

作者： Arora, Rashmi Kaur, Arvinder Guru Tegh Bahdur Institute of Technology New Delhi India Guru Gobind Singh Indraprastha University New Delhi India

ISBN: (纸本)9789819909803

Issue tracking systems (ITS) are used by organizations to organize and document the work of their projects. GitHub, GitLab, and Bugzilla are among the ITSs that have been explored thus far. Jira is one of the most extensively used ITSs in practice. Jira includes a lot of information on many projects. Each project confronts a variety of difficulties, including bug reports, improvements to already-existing features, features for a newer version, and completed jobs. Since each sort of problem has a unique set of characteristics, gathering such a large amount of data manually would be laborious, potentially fault-prone, and time-consuming. To eliminate errors brought on by human error and enhance accuracy, our main goal is to automate compiling bug reports. This paper proposed an automated bug report extraction approach called BugFinder. Our technique is implemented in Python and pulls data from the Jira repository. We used an automated technique to retrieve issues or bug reports from 15 projects and extracted 176,953 issues from the Apache JIRA repository and out of 103,018 are bug reports. The produced reports include issue characteristics such as key, priority, resolution, summary, and numerous more attributes. These reports can be used to identify various sorts of defects, such as security, memory, aging defects, and so on. The result also demonstrates that the number of bugs in Brooklyn is 72%, which is the highest among all the given projects, and the Lucene project is 41%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Python

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