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International Conference on Contemporary Computing and Informatics (IC3I)

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

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

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.

关键词： Vibrations Accuracy Roads Transportation Machine learning Real-time systems Safety Maintenance Smart phones Monitoring

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Video Summarization using Speech Recognition and Text Summarization

Video Summarization using Speech Recognition and Text Summar...

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Emerging technology (INCET), International Conference for

作者： Tirath Tyagi Lakshaya Dhari Yash Nigam Renuka Nagpal Department of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh India

Videos on the internet have been increasingly becoming the chief source of knowledge and information in today’s digital age. However, with increasing length of videos and diminishing time to spare in everyone’s lives, a need has emerged for Video Summarization tools that can provide a good summary about the content of videos without the need to watch videos in their entirety. In this paper, we introduce a two-fold approach to fetch the subject matter of videos through effective summarization. The employed approach comprises of two phases: the first phase involves performing speech-to-text conversion using an Automatic Speech Recognition(ASR) system based on a Convolutional Neural Network(CNN) for generating respective transcripts for input videos while the second phase involves performing Extractive Text Summarization to summarize the text generated by extracting the important information.

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OntoBlock: a novel ontological-based and blockchain enabled spectrum sensing framework for detection of malicious users in cognitive radio internet of things (CR-IoT) networks

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International Journal of Information technology (Singapore) 2024年第6期16卷 3913-3921页

作者： Marriwala, Nikhil Kumar Shukla, Vinod Kumar Raju, Ayalapogu Ratna Panda, Sunita S, Suma Purad, Hemavati C. Electronics and Communication Engineering Department University Institute of Engineering and Technology Kurukshetra University Haryana Kurukshetra India Information Technology Head of Academics–School of Engineering Architecture Interior Design Amity University Dubai Dubai International Academic City Dubai United Arab Emirates Department of Computer Science and Engineering Mahatma Gandhi Institute of Technology Hyderabad India Department of Electrical Electronics and Communication Engineering GITAM School of Technology Bengaluru Campus Karnataka Bengaluru India CMR Technical Campus Hyderabad India Department of AIML BITM Karnataka Ballari India

CR-IoT networks can benefit from the idea of blockchain technology to achieve accurate decisions in order to provide very high-accuracy spectrum sensing in the network. It allows for multi-user cooperation as the sensing readings are being exchanged in the spectrum. It leads to security issues in CR-IoT networks. Thus, in order to increase the quality of decision-making in spectrum sensing, the given paper introduces an ontological framework with a blockchain-enabled system to detect malicious users in networks. The proposed framework first captures network packets from available Wi-Fi, followed by the creation of blocks with private, public, and hash keys. The keys are used to encode information about the nodes, such as confidential information and their location, data relative to users, their position where packets are delivered, etc. This is subsequently followed by the generation of network ontology, which includes parsing, computation of similarity, and reduction in clustered packets. Finally, the performance of the suggested mechanism is validated using a set of evaluation measures such as authentication rate, intrusion rate, average network throughput time consumption, and energy efficiency. As seen from the findings, it is evident that the proposed framework performs better than existing recent studies, thereby improving the performance of CR-IoT networks. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Blockchain Cognitive radio Internet of things (IoT) Ontology Spectrum sensing

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Multi-frame Correlated Representation Network for Video Super-Resolution

Multi-frame Correlated Representation Network for Video Supe...

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2023 IEEE International Conference on computer, Information, and Telecommunication Systems, CITS 2023

作者： Jin, Shuo Liu, Meiqin Guo, Yu Yao, Chao Obaidat, Mohammad S. Institute of Information Science Beijing Jiaotong University Beijing China School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China King Abdullah Ii School of Information Technology The University of Jordan Amman11942 Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The Amity University UP Noida201301 India

ISBN: (纸本)9798350336092

Video super-resolution aims to reconstruct the high-resolution video from its degraded observation. Existing methods primarily utilize explicit alignment strategies, including optical flow and motion compensation, and implicit alignment strategies, including deformable convolution and non-local attention, to model the temporal information. However, these alignment strategies with motion estimation and motion compensation (MEMC) introduce inaccurate inter-frame information to some extent, which largely affects the reconstruction performance. To avoid the error compensation information, we propose to model the temporal information from the perspective of the self-similarity between frames and design a multi-frame correlated representation network (MCRNet) for video super-resolution. To address the issue of temporal information distortion caused by large-scale pixel displacement and object occlusion, MCRNet extracts temporal information through similar regions on multiple frames, which are aggregated with allocated weights for information compensation. Moreover, we design a multi-scale non-local information fusion module for non-local correlation matching of spatio-temporal features in the multi-scale space, which maintains the scale consistency of spatio-temporal features. Experimental results indicate that MCRNet achieves promising gains over other competing methods which employ explicit and implicit alignment strategies on different datasets. © 2023 IEEE.

关键词： Alignment

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X-Pand: explainable random forest with feature selection for COVID-19 like pandemics in smart cities

X-Pand: explainable random forest with feature selection for...

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8th IET Smart Cities Symposium (SCS 2024)

作者： Pronaya Bhattacharya Ashwin Verma Ashwani Kumar Tamoghna Mukherjee Bharat Bhushan Pushan Kumar Dutta Amity School of Engineering and Technology Amity University Kolkata West Bengal India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad Gujarat India School of Computer Science Engineering and Technology Bennett University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

ISBN: (数字)9781837243105

The criticality of COVID-19 has burdened our healthcare resources. Thus, there is a growing need to advance our healthcare frameworks. Recently, with the rise of Explainable Artificial Intelligence (XAI) models in healthcare that leverage interpretability and solve the black-box nature of AI, healthcare frameworks and designs in smart cities have improved many folds. To address the crisis in light of future COVID-19-like pandemics in smart cities, we propose a case-study, X-Pand, that utilizes an XAI framework over Random Forest (RF) algorithm for feature selection in COVID-19 detection. Unlike recent schemes that are mostly non-interpretable, X-Pandemphasizes transparency and interpretability, crucial for gaining actionable insights and trust in automated health diagnostics. We present a feature ranking mechanism, which integrates Shapley Additive Explanations (SHAP) for enhanced decision tree interpretability, optimizing feature selection and improving diagnostic accuracy. We evaluate X-Pandon a comprehensive COVID-19 clinical spectrum dataset, demonstrating superior performance in terms of accuracy, sensitivity, and specificity compared to traditional models like XGBoost, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). Our findings reveal that X-Pandnot only achieves higher prediction accuracy but also offers clearer justifications for its decisions, thus enabling more informed and confident clinical decisions.

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Systematization in Asr Systems: A Review

SSRN

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SSRN 2024年

作者： Joshi, Jahanvi Sharma, Dolly Dept. of Computer Science and Engineering Amity School of Engineering and Technology Amity University Uttar Pradesh Uttar Pradesh Noida201301 India

Speech analysis and its recognition play an important role in the advancement of race driven by the primary form of communication, i.e. speech. Automatic speech recognition works on the principle of taking either spoken utterances or text as input, followed by optimum interpretation of each word and analyzed speech as output. The objective of this review paper is to study the already existing technologies of ASR systems and to map out its advantages and disadvantages with needs of today. Optimal ways of finding successful and future oriented models have been discussed in this paper. They are minimum to no training data requirements, minimum operational time, use of complex algorithm to minimize error, low word error rate, multi-speaker applicable, noise and silence detection authenticated, and acceptances to unseen or new data. This paper compares famous ASR models and brings in light the huge success possible in the field of multi-model ASR system(s) technique. © 2024, The Authors. All rights reserved.

关键词： Character recognition

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Internet of Medical Things: A CT Image Denoising in Tetrolet Domain 5

Internet of Medical Things: A CT Image Denoising in Tetrolet...

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5th International Conference on Information Systems and computer Networks, ISCON 2021

作者： Diwakar, Manoj Pandey, Richa Sharma, Ritik Saun, Sapna Singh, Prabhishek Pandey, Neeraj Kumar Graphic Era Deemed to Be University Department of Computer Science Engineering Uttarakhand Dehradun India Graphic Era Hill University Campus-Haldwani Department of Computer Science Engineering Uttarakhand India Amity University Uttar Pradesh Amity School of Engineering Technology Department of Computer Science Engineering Noida India DIT University School of Computing Dehradun India

ISBN: (纸本)9781665403412

The Internet of Medical Things (IoMT) is a huge community of linked medical equipment and technologies that communicate with a large number of Servers over the internet to provide a variety of services such as medical image denoising. Projection-based reconstructed images in computed tomography (CT) are noisy due to thermal noise and electrical noise, both of which are approximately AWGN in magnitude. Because of the variety of clinical features and textures included in CT reconstructed images, denoising is a difficult process. It is possible to estimate the true noise of a single image, and then to minimize both the real noise and the additional noise by reconstructing CT images. A unique modified approach is proposed in this work, in which the noise will be calculated actual as well as added, followed by a proposed method for denoising, which is based on thresholding in tetrolet domain. Different denoising schemes are used in the experiments, and the results are compared to one another. Experimentation has revealed that both in terms of PSNR and visual quality, the suggested technique yields promising results that are favorable to both. © 2021 IEEE.

关键词： Image denoising

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X-Pand: Explainable Random Forest with Feature Selection for COVID-19 like Pandemics in Smart Cities 8

X-Pand: Explainable Random Forest with Feature Selection for...

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8th IET Smart Cities Symposium, SCS 2024

作者： Bhattacharya, Pronaya Verma, Ashwin Kumar, Ashwani Mukherjee, Tamoghna Bhushan, Bharat Dutta, Pushan Kumar Amity School of Engineering and Technology Amity University West Bengal Kolkata India Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahmedabad India School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9781837242672

The criticality of COVID-19 has burdened our healthcare resources. Thus, there is a growing need to advance our healthcare frameworks. Recently, with the rise of Explainable Artificial Intelligence (XAI) models in healthcare that leverage interpretability and solve the black-box nature of AI, healthcare frameworks and designs in smart cities have improved many folds. To address the crisis in light of future COVID-19-like pandemics in smart cities, we propose a case-study, X-Pand, that utilizes an XAI framework over Random Forest (RF) algorithm for feature selection in COVID-19 detection. Unlike recent schemes that are mostly non-interpretable, X-Pand emphasizes transparency and interpretability, crucial for gaining actionable insights and trust in automated health diagnostics. We present a feature ranking mechanism, which integrates Shapley Additive Explanations (SHAP) for enhanced decision tree interpretability, optimizing feature selection and improving diagnostic accuracy. We evaluate X-Pand on a comprehensive COVID-19 clinical spectrum dataset, demonstrating superior performance in terms of accuracy, sensitivity, and specificity compared to traditional models like XGBoost, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP). Our findings reveal that X-Pand not only achieves higher prediction accuracy but also offers clearer justifications for its decisions, thus enabling more informed and confident clinical decisions. © The Institution of engineering & technology 2024.

关键词： Diagnosis

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Exploring Machine Learning to Predict Brain Tumour Disease 7

Exploring Machine Learning to Predict Brain Tumour Disease

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

作者： Sharma, Sukrati Sunaina Shivahare, Basu Dev Singh, Kirti Singh, Prabhishek Bibhu, Vimal Just Dial Karnataka Bangalore India Amity University Department of Mathematics Uttar Pradesh Greater Noida India Galgotias University School of computer science & Engineering Uttar Pradesh Greater Noida India Bennett University School of computing science & Engineering Uttar Pradesh Greater Noida India Noida International University School of Engineering & Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

The demand of AI is increasing day by day in the medical field. Machine Learning (ML) is subset of AI. ML has become popular tool and widely used to detect several medical diseases at higher accuracy. Promising accuracy of ML algorithms depend on training of digital images like CT, MRI, X-rays etc. In this paper, CNN model is proposed to classify brain tumour into malignant and non-malignant class and comparative study using diverse ML algorithm is conducted and their accuracy is compared. This paper explores role of ML in clinical diagnosis, accurate prediction and clinical analysis regarding brain tumour disease based on various brain tumour dataset like figshare, brats, Kaggle. Proposed CNN model has achieved 98% promising accuracy. © 2024 IEEE.

关键词： computerized tomography

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Artificial Intelligence in System and Software engineering for Auto Code Generation

Artificial Intelligence in System and Software Engineering f...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Ghai, Anupriya Sharma Rawat, Vandna Gupta, Vishan Kumar Ghai, Kapil Graphic Era Hill University School of Computing Dehradun India Graphic Era Deemed to be University Science Engineering Department of Computer Dehradun India Amity University. Punjab School of Engineering and Technology Mohali India Graphic Era Hill University Department of Chemistry Dehradun India

ISBN: (纸本)9798350378092

Artificial Intelligence has profoundly impacted system and software engineering by facilitating the automation of complicated tasks, reducing errors, and accelerating the development process. AI-driven tools help enhance efficiency, improve code quality, and promote the generation of more adaptive and innovative software solutions. The present research uses AI-generated tools to explore how AI transforms code synthesis. The study will include a comparative analysis of the performance of manual coding and other automated methods to explore how AI can accelerate code synthesis and make modern web development more competent or effective. It is suggested in the research that with algorithmic and contextual consideration, the quality of code developed by the AI would be higher in most cases than by people due to lower error rates and shorter synthesis. Overall, the central conclusion of the research is that AI plays a crucial role in transforming systems and software engineering. The paper also includes information on ethical dilemmas, robust validation mechanisms, conservative modes, and current research. © 2024 IEEE.

关键词： Software quality

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