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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Raturi, Ayush Joshi, Kireet Jain, Paras Gupta, Vishan Kumar Gupta, Avdhesh Meena, Jaishree Graphic Era Deemed to be University School of Computer Science & Engineering Dehradun India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Vit Bhopal University School of Computing Engineering and Artificial Intelligence MP Bhopal India Amity University Punjab Amity School of Engineering & Technology Punjab Mohali India Akg Engineering College Department of Cse UP Ghaziabad India Amity School of Biological Sciences Amity School Punjab Mohali India

ISBN: (纸本)9798350376685

Brain tumors are quite formidable primarily because they can occur at any place within the brain and may also exhibit dissimilar behavior patterning. The disease in its initial stage is not very dangerous, if it is not treated then the result is a lot of damage to the brain. To overcome these challenges, further research and development is needed and more specifically the implementation of new technologies. This paper seeks to contribute to these goals by developing a deep learning convolutional neural network with transfer learning approach, that can help in detection of brain tumors with high accuracy and efficiency. In the long run, it should help in the early diagnosis and timely appropriate treatment of patients which should somehow contribute to the development of neurooncology. This study proposes a model that involves MRI for detection of brain tumors through the application of deep learning. The high performance of this model is attributed to the transfer learning technique that has been applied on VGG16 trained on 3000 medical MRI images of the human brain. The proposed model provided quite satisfactory accuracy of 99%. 33% on this dataset. Using this automated detection model, it is possible for the healthcare professionals to be able to predict the presence of brain tumors quite well using this system. © 2024 IEEE.

关键词： Convolutional neural networks

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Design and Architectural Implementation of Consortium Blockchain Based Framework for Open Banking Customer Consent and Data Handling

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SN computer science 2024年第2期5卷 271页

作者： Ghosh, Abir Mukhopadhyay, Indraneel Chakraborty, Subhalaxmi University of Engineering and Management West Bengal Kolkata India Amity School of Information Technology Amity University West Bengal Kolkata India Department of Computer Science University of Engineering and Management West Bengal Kolkata India

Presently blockchain-based open banking approaches have gained immense popularity in financial sector. In open banking, customer consent is mandatory by regulation before any customer data can be shared by bank with Third Party Providers (TPP) following strict security protocols. Recent study shows that customer remains unaware about private data leakages where data is shared by bank not adhering agreement between bank and customer. To mitigate such type of problem, an innovative consortium permissioned blockchain-based structure comprising banks, regulators, Third Party Providers, and customers is addressed in this proposed work. The implemented framework manages consent handling and data sharing under the gamut of blockchain security. Security analysis of the framework proves it to be secure from threats and performance evaluation of the framework is satisfactory with more than fifty customer data parameters and ten nodes of blockchain. This blockchain based framework traces data sharing non-compliances and reports it real-time basis to customer. Thus it enhances the transparency in the whole open banking data sharing process and boosts customer confidence to adopt open banking practices. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Bank Blockchain Consent management Customer trust Data violation Open banking Regulator Third party providers

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A Short Survey of Elucidating the Emotion Recognition Methodologies Using Facial Images and EEG Signals 4th

A Short Survey of Elucidating the Emotion Recognition Method...

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4th International Conference on Advanced Computational and Communication Paradigms, ICACCP 2023

作者： Kaur, Dilsheen Misra, Anuradha Vyas, O.P. Computer Science and Engineering Amity School of Engineering and Technology Amity University Lucknow Campus India Information Technology Indian Institute of Information Technology Prayagraj Allahabad India

ISBN: (纸本)9789819942831

Since the high demand for Human–computer Interaction (HCI), developing an automated model for recognizing facial gestures or emotions becomes challenging. Some experts have used facial images as a constructive part of recognizing different emotions of humans. Simultaneously, emotions can also be classified by using some electrical signals such as Electrocardiogram (ECG), Electroencephalogram (EEG), Electromyogram (EMG), and so on. Among all these signals, EEG captures the major information of brain activities. Recently, along with face images, EEG signal plays a pivotal role in emotion recognition. Though these methods have been estimated the promising results, the real-world implications and diverse feature identification of human states are still in place to gain more attention. Considering these factors, this survey aims to review the literature for analyzing emotion recognition performance using EEG signals and facial images. It also demonstrates the techniques that are utilized in machine learning and deep learning as well. The drawback of such implemented models is discussed, leading to future development. Subsequently, the survey part is followed by exploring the chronological review of the emotion recognition work, dataset utilization, methodologies employed, and experimental analysis with divergent metrics and features, and challenges. Finally, the research challenging factors provoke to raise the novel effective system for emotion recognition using facial images and EEG signals. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electrocardiograms

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Towards Intelligent Attendance Monitoring for Scalable Organization with Hybrid Model Using Deep Learning

Towards Intelligent Attendance Monitoring for Scalable Organ...

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Srivastava, Akhilesh Kumar Mishra, Chandrahas Mishra, Anurag Srivastava, Atul Department of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Computer Science ABES Engineering College Ghaziabad India Department of Computer Science KIET Group of Institutions Ghaziabad India Amity School of Engineering and Technology AUUP Lucknow India

ISBN: (纸本)9789819706402

Face recognition technology using AI has seen paradigm shift in the evolving world. Automatic attendance monitoring using real-time face identification is a solution to handle attendance in any small/large as well as scalable organization. Traditional methods in the organization involve calling names or signing sheets with individuals, which is a very time-consuming process and provides insurance. This is also subjected to manual errors. Automation of attendance recording and monitoring through face recognition is a process of identifying the face for taking attendance by using the image of the human face as biometric parameter captured through a surveillance camera in the premise. This article presents an effective way of attendance monitoring by making use of deep learning technology and compares its results with the state-of-the-art approaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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A Multi-model Software Defect Recommendation System 7

A Multi-model Software Defect Recommendation System

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

作者： Suhag, Vikas Dubey, Sanjay Kumar Sharma, Bhupendra Kumar Amity University Amity School of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Noida India Northern India Textile Research Association Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350350067

The proposed recommendation system has integrated data collection framework, which can collect repository source data and thus generate base dataset for machine learning and artificial neural network models. So proposed framework provides an end-to end solution for software defect recommendation on python projects in GitHub repository. Recommendation system is comprised of multiple components like data collection system, hybrid feature selection strategy, machine learning models and deep learning models, commit monitor. Framework has been validated on several projects from GitHub repository and outcome of recommendation is favourable for usage in real projects. © 2024 IEEE.

关键词： Contrastive Learning

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Real-Time Road Damage Detection and Infrastructure Evaluation Leveraging Unmanned Aerial Vehicles and Tiny Machine Learning

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IEEE Internet of Things Journal 2024年第12期11卷 21347-21358页

作者： Khan, Muhammad Waseem Obaidat, Mohammad S. Mahmood, Khalid Batool, Dania Badar, Hafiz Muhammad Sanaullah Aamir, Muhammad Gao, Wu Comsats University Islamabad Sahiwal Campus Department Of Computer Science Sahiwal57000 Pakistan Superior University Department Of Computer Science Lahore54000 Pakistan The University Of Jordan King Abdullah Ii School Of Information Technology Amman11942 Jordan University Of Science And Technology Beijing School Of Communication And Computing Engineering Beijing100083 China Amity University Amity School Of Engineering And Technology Uttar Pradesh Noida201301 India National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan University Of Palermo Department Of Engineering Palermo90133 Italy Emerson University Department Of Emerging Computing Technologies Multan60000 Pakistan Northwestern Polytechnical University Center For Bio-Inspired System-on-Chip School Of Computer Science Xi'an710060 China

Road damage detection (RDD) through computer vision and deep learning techniques can ensure the safety of vehicles and humans on the roads. Integrating unmanned aerial vehicles (UAVs) in RDD and infrastructure evaluation (IE) has also emerged as a key enabler, contributing significantly to data acquisition and real-time monitoring of road damages, such as potholes, cracks, and surface anomalies, facilitating proactive maintenance and improved road conditions. These UAVs are low-powered and resource-constrained devices that work autonomously to perform pattern detection and decision making leveraging tiny machine learning (Tiny ML) algorithms. These Tiny ML algorithms are designed to run on edge devices, IoT devices, UAVs, etc. In this study, the RDD2022 data set collected using UAVs and dashboard cameras of vehicles was utilized to train pure and mixed models that exhibit class instance imbalance in certain classes which is addressed by implementing data augmentation as a regularization technique. State-of-the-art two-stage detectors: Faster R-CNN ResNet101 and one-stage detectors: SSD MobileNet V1 FPN, YOLOv5, and Efficientdet D1 are employed. The results indicate that the two-stage detector achieved an impressive mean average precision (mAP) of 88.49% overall and 96.62% for focused classes. Notably, the state-of-the-art Efficientdet D1 approach achieved a competitive mAP of 86.47% overall and 95.12% for focused classes, with significantly lower computational cost. These findings highlight the potential of advanced object detection techniques, particularly Efficientdet D1, to enhance the accuracy and efficiency of RDD systems, thereby improving passenger safety and overall performance. © 2014 IEEE.

关键词： Object detection

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Securing the Future of IoT: A Comprehensive Framework for Real-Time Attack Detection and Mitigation in IoT Networks 14

Securing the Future of IoT: A Comprehensive Framework for Re...

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

作者： Srivastava, Aviral Jain, Usha Amity University Rajasthan Amity School of Engineering and Technology Jaipur India Manipal University Jaipur Department of Computer Science and Engineering Jaipur India

ISBN: (纸本)9798350335095

Increased security complexity and variety come with IoT devices. Existing security solutions generally fail to handle multiple difficulties, particularly in real-time attack detection and response. This research introduces a novel, comprehensive architecture that synergistically incorporates various cutting-edge security measures to protect IoT networks from all cyber threats. The proposed framework incorporates NodeMCU ESP8266-based real-time response, safe boot, hardware-based security modules, and zero trust architecture. It recommends incorporating blockchain for security, real-time logging and monitoring, machine learning-assisted intrusion detection, and indicators of compromise (IOCs) for incident response. For improved malware analysis and threat mitigation, integration of APIs for smooth communication with third-party services such as VirusTotal and Hybrid Analyzer is suggested. This paper also discusses data encryption, updates and patching, identity and access management, risk assessment and threat modelling, incident response and recovery, and privacy. This holistic approach to IoT security provides a robust, flexible, and scalable framework to address the changing threat landscape and growing complexity of IoT ecosystems. The proposed methodology defends IoT networks against sophisticated cyberattacks through careful analysis and empirical validation. In an increasingly interconnected world, this research could be useful for practitioners, academics, and stakeholders aiming to strengthen IoT infrastructure resilience. © 2023 IEEE.

关键词： Blockchain

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A Logistic Regression and Decision Tree Based Hybrid Approach to Predict Alzheimer's Disease

A Logistic Regression and Decision Tree Based Hybrid Approac...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Tushar Patel, Richa Kamleshkumar Aggarwal, Eshant Solanki, Kamna Dahiya, Omdev Yadav, Suman Avdhesh School of Computer Science and Engineering Lovely Professional University Punjab Kapurthala India University Institute of Engineering Anf Technology Maharshi Dayanand University Department of Computer Science and Engineering Haryana Rohtak India Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350323917

Alzheimer's disease is a degenerative neurological disorder that typically impacts individuals over the age of 65, causing damage to the brain and resulting in challenges with memory, cognition, and behavior. Although there is currently no cure for this condition, various therapies and medications exist to manage symptoms and slow the progression of the disease. Individuals with Alzheimer's disease experience cognitive impairment, changes in behavior, and communication difficulties, often leading to psychological and behavioral issues like anxiety, aggression, and depression. In this research paper, a novel approach to predicting outcomes using MRI data sets from the Open Access Series of Imaging Studies (OASIS) is proposed. The method involves data exploration, data preprocessing, and the development of a hybrid model that utilizes both Logistic Regression and Decision Tree algorithms. Through rigorous testing, the proposed hybrid model demonstrated superior accuracy compared to existing models, achieving an overall accuracy rate of 96%. © 2023 IEEE.

关键词： Decision trees

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Stroke Prediction using Machine Learning Methods 12

Stroke Prediction using Machine Learning Methods

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12th International Conference on Cloud Computing, Data science and engineering, Confluence 2022

作者： Gupta, Saumya Raheja, Supriya Amity University Amity School Of Engineering And Technology Computer Science Engineering Noida India

ISBN: (纸本)9781665437011

Stroke, a medical emergency that occurs due to the interruption of flow of blood to a part of brain because of bleeding or blood clots. Worldwide, it is the second major reason for deaths with an annual mortality rate of 5.5 million. Every year, more than 15 million people worldwide have a stroke, and in every 4 minutes, someone dies due to stroke. A stroke is generally a consequence of a poor style of living and hence, preventable in up to 80% of the cases. Therefore, the prediction of stroke becomes necessary and should be used to prevent permanent damage by stroke. The current work predicted the stroke using the different machine learning models namely, Gaussian Naive Bayes, Logistic Regression, Decision Tree Classifier, K-Nearest Neighbours, AdaBoost Classifier, XGBoost Classifier, and Random Forest Classifier. The paper presents the comparison among all machine learning algorithms. Analysis of results revealed that the AdaBoost, XGBoost and Random Forest Classifier made the least value of incorrect predictions and had the greatest accuracy scores 95%, 96% and 97% respectively. Hence, they were the best suited model for stroke prediction and can feasibly be used by physicians to predict stroke in real world. © 2022 IEEE.

关键词： Forecasting

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A Development of Designing a Sensor N/W for Retrieving the Data from WSN via SBT 4

A Development of Designing a Sensor N/W for Retrieving the D...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Thangaraj, S. John Justin Kumar, Neelam Sanjeev Saranya, A. Bhat, C. Rohith Kareem, Doaa Saadi Shareef, Hayder Saveetha School of Engineering Department of Cse Chennai India Srm Institute of Science & Technology Department of Computer Science & Engineering Chennai Vadapalani26 India Amity University Amity School of Engineering and Technology Mumbai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India National University of Science and Technology College of Health and Medical Technology Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

Search engines are essential for getting information from web sites in the internet age. They use a variety of ranking algorithms, depending on content-based information extraction techniques or statistical searching strategies, to organise the retrieved results. It is still difficult, nevertheless, to comprehend the underlying information on each web page without opening each one individually. This challenge motivates the concept and implementation of an object-attribute-value (O-A-V) information extraction system as a web model. To help with future searches, this model functions as a user dictionary by helping to filter search terms in queries. To evaluate the first model, natural language processing (NLP) and semantic web (SW) queries in the form of English phrases are employed. Similar to this, content-based picture information extraction and retrieval against user queries is used by image search engines like Google Images. Using picture descriptions, a domain ontology is constructed in order to close the semantic gap between user expectations and image retrieval outcomes. The second suggested model looks at subject-predicate-object (S-P-O) interactions by analysing natural language user queries using an NLP parser algorithm. This extraction builds upon the O-A-V web model, which is ontology-based. The article presents a SPARQL auto-query generation module to auto-generate SPARQL questions by utilising S-P-O extraction and taking into account the intricacy of creating SPARQL queries from the standpoint of a user. The auto-generated SPARQL query is used to obtain photos after these queries are placed on the ontologies. Systems for retrieving documents and images were developed and evaluated against industry standards in order to provide answers to these problems. The outcomes show improvements over baseline systems. © 2024 IEEE.

关键词： Ontology

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