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An AutoML Approach Integrated with Live Weather Data in Rain Forecasting System (RFS) 1

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8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Mazahir Mehdi Zaidi, Syed Rizvi, Sheenu Arora, Deepak Tiwari, Shivam Department of Computer Science and Engineering Amity School of Engineering & Technology Lucknow Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (数字)9789819777105

ISBN: (纸本)9789819777099

The increasing climate unpredictability highlights the critical need for accurate rain prediction methods. Traditional approaches often struggle to adapt and accurately forecast rain across varied climatic conditions. This research work investigates the fusion of automated machine learning (AutoML) with real-time weather data to tackle these challenges. Previous methodologies have relied on manual algorithm selection and optimization, leading to inefficiencies and suboptimal outcomes. In contrast, the proposed AutoML-based approach automates algorithm selection and optimization, resulting in heightened accuracy and efficiency. Integration of diverse meteorological data streams and advanced preprocessing techniques enhances data quality for model training. Evaluation against traditional methods showcases significant enhancements in rain prediction accuracy, with scores ranging from 80.60 to 86.09% across distinct algorithms. Specifically, the Random Forest Classifier achieved an accuracy score of 85.43% and an AUC score of 0.88, Gaussian NB achieved 80.60% accuracy and 0.82 AUC, K Neighbors Classifier achieved 82.89% accuracy and 0.82 AUC, and XGBoost achieved 86.09% accuracy and 0.89 AUC. The developed approach not only advances rain forecasting but also offers potential applications in agricultural planning, urban water management, and disaster preparedness. Future research avenues may explore further enhancements to the AutoML framework and its extension to other environmental prediction domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Rain

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Decentralized Identity Management Using Self-Sovereign Identity Approach Through Blockchain 1

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8th International Conference on Inventive Communication and Computational Technologies, ICICCT 2024

作者： Anas, Khan Mohammad Rizvi, Sheenu Arora, Deepak Tiwari, Shivam Department of Computer Science and Engineering Amity School of Engineering & Technology Lucknow Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (数字)9789819777105

ISBN: (纸本)9789819777099

Exploring the transformative potential of Self-Sovereign Identity (SSI) in decentralized identity management, utilizing blockchain technology, this research analyzes the evolution from centralized to user-centric online identity paradigms. Addressing issues related to centralized authorities, SSI emerges as a solution. Recent statistics reveal a surge in identity-related fraud and privacy concerns amid escalating data breaches and cyber threats. The methodology involves in-depth analysis and visualization of SSI components like decentralized identifiers, verifiable credentials, and presentations, alongside the integration of a decentralized key management system, identifying the critical role of trust infrastructure. Comparative analysis of established SSI systems and decentralized identifier (DID) methods contributes to the development of secure, user-centric systems, countering risks associated with centralized identity management and laying a foundation for robust decentralized identity infrastructures in the digital field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decentralized finance

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Impact of COVID-19 Variants Using Multi-Criteria Selection Approach

Impact of COVID-19 Variants Using Multi-Criteria Selection A...

引用

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

作者： Sagar, Shrddha Kumar, Avadhesh Singh, Sofia Shukla, Garima Upadhyay, Diwakar School of Computer Science and Engineering Galgotias University Uttar Pradesh India Amity School of Engineering and Technology Amity University Department of Artificial Intelligence Uttar Pradesh Noida India Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Maharashtra Mumbai India School of Computing Graphic Era Hill University Haldwani India

ISBN: (纸本)9798331517953

The aim of the proposed work is related to the detailed study of various types of COVID-19 along with its symptoms. This study utilized a retrospective approach to the COVID data from 2019 to 2021 to assess the severity level of the various types of COVID-19. After a detailed analysis, we have identified seven symptoms which are as follows: breathing, chest pain, fever, body ache, sore throat and cough, tiredness, and loss of taste/smell. In this study, the prediction of the COVID-19 severity level is made using a fuzhzy multi-objective optimization based on ratio analysis (fuzzy MOORA) approach. The proposed model simultaneously predicts the severity level of COVID-19 with multiple criteria. The severity levels listed on government-approved websites for various types of COVID-19 are considered for validating the proposed methodology's result. The highest overall performance index value is used to decide. As a result, the overall performance index value has been achieved and moreover, we can predict the severity level of different forms of COVID-19 using this strategy. After the implementation of our proposed approach, we got the overall performance index value of delta T4 (0.433086). The Coronavirus Virus 2019 (COVID-19) pandemic is on the increase for over two years. The World Health Organization (WHO) statistics show that approximately 260 million confirmed cases have been documented, with over five million deaths. In our work, we have implemented a fuzzy MOORA approach for predicting the severity level of the different types of COVID-19. The overall performance index value of each COVID-19 is shown in the table. Table 1.2 demonstrates that Delta T4 is ranked 1st, Gamma T3 is ranked 2nd, Beta T2 is ranked 3rd, Alpha T1 4th and Omicron T5 has the least rank. © 2024 IEEE.

关键词： COVID-19

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MRI Based Health Detection Using Machine Learning Techniques 14

MRI Based Health Detection Using Machine Learning Techniques

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Ranjai, Rishab Gupta, Sumita Amity School of Engineering and Technology Amity University Uttar Pradesh Department of Computer Science & Engineering U.P. Noida India

ISBN: (纸本)9798350344837

Health has a significant influence on one's wellbeing and quality of life. By assisting medical practitioners, suggesting possible therapies, and allowing early illness identification, machine learning techniques have the potential to completely transform the healthcare sector. This research assesses many machine learning approaches, such as Decision Tree classifier, Random Forest classifier, Logistic Regression, and Support Vector Machine, for identifying heart health concerns. It also offers a Convolutional Neural Network (CNN) model for brain tumor diagnosis. CNNs may detect tiny anomalies that may escape human inspection because of their expertise in processing medical pictures, such as MRI scans. The suggested 11-layer CNN model for cancer diagnosis has a 97.7% accuracy rate, a 96.9% recall rate, and a 1.36% false positive rate. Furthermore, by evaluating large amounts of patient data to pinpoint those who are at risk, machine learning algorithms also significantly contribute to the prediction of cardiac disease. © 2024 IEEE.

关键词： Logistic regression

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Fake News Detection Using Boosting Ensemble Method 14

Fake News Detection Using Boosting Ensemble Method

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Kaur, Kirandeep Kashyap, Nirbhay Amity School of Engineering and Technology Amity University Computer Science Department Uttar Pradesh Noida201301 India

ISBN: (纸本)9798350344837

Around the globe, Fake news is a raising concern and has several negative repercussions on people, societies, and countries. It has several detrimental effects on society, including disseminating false information and causing confusion, inciting social unrest and division, eroding credibility and confidence, endangering public health, and causing political instability. To protect people from damage, preserve informational accuracy, stop the spread of hate speech, support a vibrant democracy, and maintain journalism's reputation, fake news detection is crucial. In this work, we attempt to tackle this problem by putting forth a method that uses machine learning classification to distinguish between authentic and fraudulent content. Natural language processing and machine learning ensemble approach are taken into consideration for this. In natural language processing, TF-IDF and Word2vec features are used and optimized using wrapper method (via RFE algorithm). Training and testing are done on 'Fake News Detection' dataset from Kaggle. Exploratory examination upon the boosting ensemble method tells that our learners perform better than other options. © 2024 IEEE.

关键词： Statistical tests

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An In-Depth Analysis of Network Security: Intrusion Detection and Prevention Systems 3

An In-Depth Analysis of Network Security: Intrusion Detectio...

引用

3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Kaur, Amritpal Tyagi, Rajesh Kumar Sinha, Shweta Vats, Prashant Amity School of Engineering and Technology Department of Computer Science and Engineering Haryana India Manipal University Department of Computer Science and Engineering Rajasthan Jaipur India

ISBN: (纸本)9798331518592

The concept of making everything easily and widely accessible has revolutionized the network industry. Even with the rapid advancements in networks and information technology, we still have difficulty guarding against attacks and theft of our resources. Small organizations might not be affected by this, but industry, businesses, and national security are all seriously affected. Every day, organizations encounter more and more dangers in the form of malware, intrusions, etc. Despite the fact that corporations have implemented a variety of defences against these types of assaults, including intrusion detection and prevention systems (IDPS), many security breaches remain undiscovered. To understand the security risks and IDPS, we will first survey common security breaches and then discuss the different opportunities and challenges in this field. In this paper, we survey the overall progress of intrusion detection systems, including existing types, techniques, and architectures found in the literature. © 2024 IEEE.

关键词： Network intrusion

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Modified Osprey-Optimized DM-CNN Model for Human Activity Recognition 6th

Modified Osprey-Optimized DM-CNN Model for Human Activity Re...

引用

6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Tyagi, Anshuman Singh, Pawan Dev, Harsh Department of Computer Science and Engineering Amity School of Engineering and Technology Lucknow Amity University Uttar Pradesh Lucknow India Department of Computer Science and Engineering Pranveer Singh Institute of Technology Uttar Pradesh Kanpur India

ISBN: (纸本)9789819778614

Human Activity Recognition (HAR) is an AI-based technique aimed at identifying and labeling human activities. It gathers activity data from sources like wearable sensors and smart devices. Essentially, it categorizes human activities based on interactions, movements, and actions. Currently, it finds widespread application across various domains, leading to continuous research efforts for practical improvements. The present study revolves around HAR, a hybrid DM-CNN model, where the activities of humans are recognized by four phases including preprocessing, segmentation, feature extraction, and recognition. Here, the hybrid DM-CNN model uses a specific set of extracted features for enhanced training and they are MTH, IGBP and hierarchy of skeleton. Most importantly, the hybrid DM-CNN model provides excellent results on human activity recognition through the development of the Modified Osprey Optimization (MOO) algorithm for fine-tuning the DM and CNN models. The specificity of the proposed model is 95%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Human form models

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Exploring Deception Techniques in Safeguarding IoT Networks from Intruders

Exploring Deception Techniques in Safeguarding IoT Networks ...

引用

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

作者： Chaturvedi, Rupal Tyagi, Neha Pateria, Jalaj Tyagi, Lalit Kumar Computer Science And Engineering Amity School Of Engineering And Technology Uttar Pradesh Noida India Computer Science And Engineering Amity School Of Engineering And Technology Uttar Pradesh Noida India Information Technology Amity Institute Of Information Technology Uttar Pradesh Noida India Chandigarh Group Of Colleges Mohali Punjab Jhanjeri India

ISBN: (纸本)9798350366846

The expansion of Internet of Things devices and its network is not only transforming across industries but also has been playing an important role in our day-to-day life. Though, this increase in the number of connected IOT devices brings the concern of different cyber threats and vulnerabilities. Gradually, attacks are getting matured day by day which compromises the security and intruders are getting easy access into the IOT environment due to traditional security mechanism. Deception technology has been an effective technique has improved security in IOT devices. Deception technology implements the deployment of honeypots, deceptive elements, breadcrumbs in required IOT environment as defence mechanism. In this study, we discuss different deceptive methods along with synthetic data generation that can be applied in multiple IOT environments to enhance the security by not only identifying anomalous behaviour but also prevent intruder to perform malicious activities. © 2024 IEEE.

关键词： Cybersecurity

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Lip Reading Into Text Using Deep Learning 14

Lip Reading Into Text Using Deep Learning

引用

14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Manglani, Krishna Bahuguna, Utkarsh Gupta, Subhash Chand Amity School of Engineering and Technology Dept. of Computer Science Uttar Pradesh India

ISBN: (纸本)9798350344837

An essential part of computer vision, lip reading, has grown significantly and is now used in autonomous driving, public safety, and hearing-impaired communication. This work provides an in-depth analysis of both conventional and deep learning-based lip reading systems, examining their development over time, difficulties encountered, and critical role in human communication. The suggested LipNet model, which leverages the complementary abilities of Long Short-Term Memory networks (LSTMs) for temporal modelling and Convolutional Neural Networks (CNNs) for spatial feature extraction, is a central focus of this research. When LipNet is tested using the GRID Corpus dataset, it outperforms baseline models in terms of performance. Saliency maps and viseme analysis highlight LipNet's ability to capture phonologically relevant areas in lip motions, providing insight into the network's attention processes. Apart from examining the importance of datasets such as OuluVs2and GRID Corpus, this study presents GhostNet, a lightweight model that tackles the requirement for effective network designs. The testing results highlight LipNet's competitive Character Error Rate (CER) and Word Error Rate (WER) in comparison to baseline models, proving that it is the best method for real-world use. The research ends with a summary of potential future developments in lip reading technology, such as modal integration, realtime applications, personalisation, ethical issues, and the goal of creating a communication environment that is more inclusive. This study highlights the effectiveness of LipNet and paves the way for future developments in the ever-evolving area of lip reading by examining a variety of methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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A Real-Time Road Anamoly Detection Using Smart Phone Sensors 7

A Real-Time Road Anamoly Detection Using Smart Phone Sensors

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Singh, Prabhat Sharma, Shilpi Amity School of Engineering and Technology Department of Computer Science Uttar Pradesh Noida India

ISBN: (纸本)9798350350067

The quality of the conventional transport system has been significantly impacted recently by the swift advancement of emerging technologies and the rapid growth of the economy. ITS which seeks to enhance the transportation network, is gaining popularity. Additionally, one of the main goals of ITS is to enhance traffic safety, and potholes seriously compromise the safety of vehicles. Therefore, by establishing a real-time pothole-detecting system and disseminating pothole information, drivers' safety may be enhanced. For assessing road conditions, we propose to analyze various supervised machine learning approaches employing mobile sensors and GPS data obtained from cellphones. Our research focuses on classifying three primary class labels: deep transverse cracks, potholes, and smooth roads. We test our hypothesis that employing information from the three sensors' axes yields more accurate results than using data from just one. We also study how well deep neural networks do in classifying different types of traffic situations. Our findings demonstrate that smartphone sensor axes outperform models using only one axis. This demonstrates that vibration signals for road abnormalities contain information throughout all three axes. We also note that substantially more accurate data classification is obtained when using neural networks. The techniques discussed here can be applied more broadly to monitor road conditions that could endanger people's safety. Commuters in addition to giving pertinent authorities maintenance information. © 2024 IEEE.

关键词： Smartphones

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