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Computational Analysis of Gold–Platinum Nanowires Subjected to Uniaxial Tensile Loading

Journal of The Institution of Engineers (India): Series D 2024年 1-7页

作者： Gupta, Mahesh Kumar Guha, Souvik Singh, Netra Pal Panwar, Vinay Department of Mechanical Engineering SRM Institute of Science and Technology Delhi-NCR Campus U.P. Modinagar Ghaziabad201204 India Department of Mechanical Engineering Netaji Subhas University of Technology Delhi110078 India

Gold–platinum (AuPt) nanosystem shows promising characteristics in catalysis, sensing and therapeutics. In this work, the impact of alloying Pt with Au on selective mechanical properties (i.e. Young’s modulus, yield strength, modulus of resilience and ultimate tensile strength) has been examined using molecular dynamic simulations. Models of AuPt nanowires were developed for discrete compositions varying from 0 to 100% Pt in Au. The effect of change in temperature on the mechanical properties of AuPt nanowires subjected to tensile loading has also been included in this study. Though the hardness and strength of Pt are superior and incomparable to Au, the addition of Pt in Au behaves differently. It has been observed that the addition of up to 25% Pt in Au (3Au–Pt) reduces both strength and elastic energy absorption, with a subsequent increase in these properties beyond this concentration. Corresponding mechanical properties approach those of pure Au when the Pt concentration exceeds 50% in Au. With further increase in content of Pt by 75% (Au–3Pt), the elastic modulus becomes comparable to that of pure Pt. However, this enhancement is accompanied by a reduction in yield strength, modulus of resilience and ultimate tensile strength. The obtained results show distinct features of the AuPt nanowires in both the elastic and plastic regions. © The Institution of Engineers (India) 2024.

关键词： Elastic moduli

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A Comparison of Machine Learning Algorithms and Transformer-based Methods for Multiclass Sentiment Analysis on Twitter 14

A Comparison of Machine Learning Algorithms and Transformer-...

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

作者： Singh, Aakash Kumar, Suresh Netaji Subhas University of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350335095

The growing number of tweets being produced on Twitter and other social media platforms has made sentiment analysis a crucial task in comprehending individuals' viewpoints and emotions. We focus on the challenge of Twitter emotion classification in this paper and perform sentiment analysis on six different emotions: joy, anger, sadness, fear, surprise, and disgust. The performance of four distinct models is studied as this topic is addressed as a multiclass classification challenge: Naive Bayes, logistic regression, SVM, and DistilBERT, a transformer-based model. Experiments are run on a dataset of over 16,000 tweets that have been annotated with six emotions: joy, anger, sadness, fear, surprise, and disgust. DistilBERT outperforms the other models with an overall accuracy of 93.5%, according to our results. The accuracy of Naive Bayes is 78%, the accuracy of logistic regression is 89%, and the accuracy of SVM is 88%. Our research demonstrates the utility of transformer-based models, specifically DistilBERT, in sentiment analysis tasks using Twitter data. The findings also highlight the significance of appropriate pre-processing techniques in the performance of traditional machine learning models. Our findings have practical implications for businesses looking to gain insights into customer opinions and preferences via social media platforms such as Twitter. © 2023 IEEE.

关键词： Sentiment analysis

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Artificial Intelligence and Natural Language Processing Inspired Chabot Technologies

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Recent Advances in computer Science and Communications 2024年第1期17卷 11-20页

作者： Singh, Deepti Manju Jatain, Aman Netaji Subhash Institute of Technology New Delhi India Department of Computer Science and Engineering & Information Technology Jaypee Institute of Information Technology Noida India Department of Computer Science and Engineering Amity University Haryana India

Chatbots use artificial intelligence (AI) and natural language processing (NLP) algorithms to construct a clever system. By copying human connections in the most helpful way possi-ble, chatbots emulate individuals and serve as virtual assistants. They easily interface and respond to customers' requests. In the modern technical environment, these conversation agents or chatbots are considered the next-generation invention. Chatbot has become more popular in the business field right now as it can reduce customer service cost and handle multiple users at a time. There are many techniques used to involve such intelligent experts in daily business. A comprehensive analysis of the methods is needed to determine the viability of the different strategies. This paper tracks the progress of this invention and further clarifies the influence of chatbots on numerous businesses. Besides, a survey of the multiple chatbot methodologies suggested by various researchers is provid-ed. Along with the survey, a chatbot e-commerce customer service is designed to provide an efficient and accurate answer for any query based on the dataset of frequently asked questions. This chatbot can reduce customer service costs and can handle multiple customers at the same time. © 2024 Bentham Science Publishers.

关键词： Machine learning

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A Practical Method for Identifying Brain Tumors Using Deep Learning 14

A Practical Method for Identifying Brain Tumors Using Deep L...

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

作者： Dua, Rajat Bhatt, Shobha Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350335095

Brain tumor detection in MRI scans has developed into a growing study field in medical imaging systems during the past few decades. Finding a brain tumor helps to pinpoint the tumor's exact size and location. This study uses image augmentation and convolution neural networks to detect brain tumors. On the Br35H dataset, the Experiment's trials are performed. The proposed model makes use of regularization techniques to lessen overfitting. With an accuracy of 98.84%, the BTD-CNN model exceeds all other cutting-edge methods. © 2023 IEEE.

关键词： Tumors

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Evaluation of Machine Learning Algorithms Performance in Comparison to Cloud Privacy Legislation 3

Evaluation of Machine Learning Algorithms Performance in Com...

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3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Patil, Sourabh Bhatnagar, Vishal Singh, Shyla Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350300710

An Intrusion Detection System (IDS) is a security tool that checks network data for any indication of malicious activity and notifies the network administrator. The proposed system puts into practice an anomaly-based IDS using a variety of supervised machine approaches. Anomaly and normal, the binary classification employed by the IDS model, are used to categorize, or identify anomalous network packets (0). The model evaluation is compiled using metrics for machine learning evaluation. The anomaly-based IDS model outperforms signature-based IDS in terms of its high computational cost, confined coverage, lack of adaptability, and high false positive rate. The dataset is utilized to train our IDS Model and it is trained and evaluated using various classifiers. Notably, the Random Forest Classifier achieves an exceptional accuracy rate of 99.92%, surpassing all other classifiers, thereby aligning with the cloud privacy legislation's objective of ensuring data confidentiality and integrity. © 2023 IEEE.

关键词： Intrusion detection

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Structural analysis using MPU6050 gyroscope 14

Structural analysis using MPU6050 gyroscope

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

作者： Narula, Rhythm Barwal, Pulkit Singal, Gaurav Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350335095

Many buildings are sensitive to earthquake and hurricanes, and it is easy to generate large deflection and dynamic response even through daily motion of a huge crowd. There are also structures with inappropriate distributions of strength and stiffness which performs poorly in case of any seismic activity and are one of the major causes of collapses leading to loss of lives. With these issues, there is a growing demand for the exploration of different methodologies to detect the prime areas in the structures that require improvement and repair, and with these early detection techniques, we can take suitable action that can, in turn, save a lot of lives. Previous research in this field mainly revolved around checking concrete strength and rust which was insufficient. Other devices which exist are expensive and they has to be equipped while constructing the building. In this paper we have summarised our study on finding the structural strength of buildings with the help of MPU6050 gyroscope. The MPU6050 gyroscope is a 3-axis gyroscope and a 3-axis accelerometer. The sensor measures the angular rotation from its axis and transfers it to the smartphone using a Bluetooth module. © 2023 IEEE.

关键词： Gyroscopes

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Detection of Schizophrenia from Actigraphy Data using UMAP Features 10

Detection of Schizophrenia from Actigraphy Data using UMAP F...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Misgar, Muzafar Mehraj Bhatia, M.P.S. Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9789380544472

Schizophrenia is a prevalent public health problem that costs both health and the economy. In most cases, there is no straightforward quantitative test for diagnosing mental health issues. Conventional methods based on patient interviews or questionnaires are still used for diagnosing mental health problems like depression and schizophrenia. This paper proposes a new approach to diagnose schizophrenia and related mental health problems by using motor activity data. The proposed framework consists of two parts: feature extraction using UMAP (Uniform Manifold Approximation and Projection) and a Neural network (NN) for classification. The results from using this framework on 24-hour motor activity data (Psykose Dataset) show high accuracy in diagnosing schizophrenia with a score of 93.89%, sensitivity of 89.04%, and specificity of 96.42%. This new approach demonstrates improved results compared to traditional methods of manual feature extraction. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Feature extraction

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Vehicular Ad Hoc Network: A Review 1

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Monika Rani, Poonam Department of Computer Science and Engineering Netaji Subhas University of Technology New Delhi India

ISBN: (数字)9789819933150

ISBN: (纸本)9789819933143

Every industry, including academia and business, has seen a major change as a result of technological advancement. In the last two decades, smart transportation has gained a lot of popularity and is now seen as a crucial component of the automotive industry. Driverless cars, autonomous vehicles, and electric vehicles are all becoming a reality these days. When it comes to road transportation, the term "smart transportation" refers to vehicular ad hoc network (VANET). A lot of the work is still only in research articles and has not yet been applied practically. When discussing VANET, the primary issue is for road user safety. Mobile vehicle intercommunication and intelligent infrastructure are used to transmit emergency information among network nodes for quick and effective decision-making. This article provides an overview of VANET, its characteristics, and application areas. Afterward paper discusses various challenges in VANET and analyzes the future scope of VANETs. In end, paper discusses some of the very popular simulators used for VANETs simulation. This survey article that covers issues, challenges, and applications of VANET can benefit society by promoting the development and deployment of VANETs, which can improve road safety, enhance transportation services, and provide economic benefits. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Vehicular ad hoc networks

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Decision-based framework to facilitate EDGE computing in smart health care

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Multimedia Tools and Applications 2024年第38期83卷 85849-85869页

作者： Singh, Simranjit Sajwan, Mohit Kukreja, Sonal Department of Information Technology Dr B R Ambedkar National Institute of Technology Jalandhar Punjab India Department of Information Technology Netaji Subhas University of Technology New Delhi India School of Computer Science Engineering and Technology Bennett University U.P. Greater Noida India

In the past few years, with the increase in population and health concerns, there has been a need for efficient health monitoring solutions that can help patients monitor their health consistently to be aware of any health risks at the initial stage. The advancement in sensing and smart technologies helps monitor human behaviors to predict health risks. In this work, a dynamic decision-based activity prediction system is proposed using Random Forest, SVM, Decision Trees, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) on an edge device. We train the models using features from the MHealth dataset, such as acceleration, rate of turn, and magnetic field, to predict activities such as standing, climbing, running, and jogging, collected from various sensors. Our framework dynamically selects between machine learning (ML) and deep learning (DL) algorithms based on real-time data size and edge device capabilities, ensuring optimal performance and resource utilization. The results for the proposed models are compared and analyzed. The experimental results indicate that among all machine learning methods, Random Forest achieves the highest overall accuracy at 98%, while in deep learning algorithms, both LSTM and GRU reach a maximum accuracy of 98%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic health record

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Credit Card Fraud Detection using Neural Embeddings and Radial Basis Network with a novel hybrid fruitfly-fireworks algorithm 3

Credit Card Fraud Detection using Neural Embeddings and Radi...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Singh, Indu Aditya, Nilesh Srivastava, Pranjal Mittal, Shivanshu Mittal, Taruvar Surin, Noel Vaibhav Delhi Technological University Department of Computer Science and Engineering India Netaji Subhas University of Technology Department of Manufacturing Processes and Automation Engineering India Netaji Subhas University of Technology Department of Information Technology India

ISBN: (纸本)9798350338607

Financial institutions are highly concerned about credit card fraud due to the growing frequency of deceitful activities linked with credit card transactions. To address this issue, a novel fraud detection system is proposed that combines advanced feature engineering and neural network architecture. The proposed approach. FFFW-RBN, employs a hybrid swarm intelligence model comprising the Fruitfly Optimization Algorithm and Fireworks Algorithm, (FFO-FWA) for feature selection, and the Neural Embeddings Generator in combination with a Shallow Radial Basis Network (RBF) for localized pattern classification. The proposed system achieves a 99.97% accuracy, 0.91 recall, and a precision of 0.94. The experimental results demonstrate that our methodology effectively and practically detects fraudulent credit card transactions. The paper presents a comprehensive evaluation of the system, including various performance metrics and a confusion matrix, highlighting its potential for real-world implementation. © 2023 IEEE.

关键词： Radial basis function networks

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