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Computational mechanical behavior of nanocrystalline copper-tantalum alloy

Materials Today: Proceedings 2022年 62卷 209-214页

作者： Kumar Gupta, Mahesh Panwar, Vinay Prasad Mahapatra, Rajendra Department of Mechanical Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi-NCR Campus Delhi-Meerut Road Modinagar Ghaziabad U.P India Mechanical Engineering Department Netaji Subhas University of Technology New Delhi Dwarka 110078 India Department of Computer Science & Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi-NCR Campus Delhi-Meerut Road Modinagar Ghaziabad U.P India

This study reports the mechanical properties of a nanocrystalline binary alloy using molecular dynamic simulation approach. The analysis has been specifically conducted under uniaxial tensile loading of the crystal lattice. The chosen nanocrystalline material was employed for the analysis of an alloy of copper (Cu) and tantalum (Ta) metals with concentration being Cu60-Ta40. The tensile behavior of the nanocrystalline alloy has been investigated at different temperatures varying from room temperature (i.e. 300 K to 1200 K, ambient pressure, and selective strain rates ranging between 1 × 1010 /sec and 2 × 1010 /sec. Initially, the perfect crystal of the alloy was modelled. The effect of change in temperature and strain rate on stress–strain response of Cu-Ta alloy has been explored. It has been noticed that with the rise in temperature from 300 K to 800 K the elastic constant, ultimate tensile strength and yield strength decreases slowly which becomes more prominent at higher temperatures i.e. 1000 K and 1200 K. Also, the behavioral shift of the stress–strain curve is noticed in an upward direction with increasing strain rates. © 2022

关键词： Molecular dynamics

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PDNet: A Novel Approach to Plant Disease Detection Using CNN and Keras

PDNet: A Novel Approach to Plant Disease Detection Using CNN...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Garima Jain Amita Shukla Anamika Chaudhary Nidhi Sharma Sovers Singh Bisht Ankush Jain Department of Computer Science and Buisness Systems Noida Institute of Engineering and Technology Greater Noida India Department of Data Science Noida Institute of Engineering and Technology Greater Noida India Department of Computer Science and Engineering Netaji Subhas University of Technology Delhi India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

This research addresses the pressing global demand for food by leveraging cutting-edge deep learning techniques for automating plant disease detection. Focusing on tomato and potato leaf diseases, the study utilized the PlantVillage dataset to compare classical machine learning methods, such as Local Binary Patterns (LBP) combined with classifiers like kNN, SVM, and Extreme Learning Machines, against modern Convolutional Neural Network (CNN) architectures. These included MobileNet, DenseNet201, ResNet50, Inception V3, and VGG16/19. A custom-designed CNN model was developed with unique configurations implemented using Keras and TensorFlow. The model was tested for binary, 6-class, and 10-class classifications, achieving remarkable accuracy rates of 99.6%, 98.4%, and 97.2%, respectively. Notably, after 50 training epochs, the model achieved a peak accuracy of 99.78% for plant disease classification. A practical web-based application was also developed, integrating the best-performing model to facilitate real-time leaf disease detection and notify farmers via email. This approach demonstrates a significant advancement in sustainable agriculture by providing an efficient, accurate, and scalable solution for early disease detection, which is critical for improving crop yield and productivity.

关键词： Training Deep learning Support vector machines Smart agriculture Plant diseases Accuracy Real-time systems Robustness Convolutional neural networks Crop yield

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technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

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computers, Materials & Continua 2021年第5期67卷 1679-1696页

作者： Siddhant Banyal Rinky Dwivedi Koyel Datta Gupta Deepak Kumar Sharma Fadi Al-Turjman Leonardo Mostarda Department of Instrumentation and Control Netaji Subhas University of TechnologyNew Delhi110078India Department of Computer Science&Engineering Maharaja Surajmal Institute of TechnologyNew Delhi110058India Department of Information Technology Netaji Subhas University of TechnologyNew Delhi110078India Research Center for AI and IoT Near East UniversityNicosiaMersin10Turkey Computer Science Department Camerino UniversityCamerino62032Italy

The COVID-19 outbreak initiated from the Chinese city of Wuhanand eventually affected almost every nation around the globe. From China,the disease started spreading to the rest of the world. After China, Italybecame the next epicentre of the virus and witnessed a very high death *** nations like the USA became severely hit by SARS-CoV-2 virus. TheWorld Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the worldhas instituted various policies like physical distancing, isolation of infectedpopulation and researching on the potential vaccine of SARS-CoV-2. Toidentify the impact of various policies implemented by the affected countrieson the pandemic spread, a myriad of AI-based models have been presented toanalyse and predict the epidemiological trends of COVID-19. In this work, theauthors present a detailed study of different articial intelligence frameworksapplied for predictive analysis of COVID-19 patient record. The forecastingmodels acquire information from records to detect the pandemic spreadingand thus enabling an opportunity to take immediate actions to reduce thespread of the virus. This paper addresses the research issues and correspondingsolutions associated with the prediction and detection of infectious diseaseslike COVID-19. It further focuses on the study of vaccinations to cope withthe pandemic. Finally, the research challenges in terms of data availability,reliability, the accuracy of the existing prediction models and other open issuesare discussed to outline the future course of this study.

关键词： COVID-19 diagnosis deep learning forecasting models machine learning metaheuristics prediction big data pandemic

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Path Texture Detection for Visually Impaired using Transfer Learning

Path Texture Detection for Visually Impaired using Transfer ...

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International Conference on Secure Cyber Computing and Communication (ICSCCC)

作者： Kanak Manjari Animesh Madhushi Verma Gaurav Singal Computer Science Engineering Bennett University Gr. Noida India Computer Science & Engineering National Institute of Technology Delhi India Computer Science Engineering Netaji Subhas University of Technology India

The computer vision technique known as object detection is used to determine the location of an object in an image or video. Recent advances in a number of areas, including computer vision, speech recognition, and natural language processing have been made using deep learning approaches. Convolution neural networks (CNNs), which have several layers, are used to detect objects. A deep learning model that uses CNN to identify various surface regions has been suggested. The study of tracks, walkways, playgrounds, highways, and muddy areas is a pertinent and significant aspect of object detection for Visually Impaired (VI) individuals. In addition to adding indirect vision to the blind individual, this model recognizes surface regions and alerts them using text-to-sound conversion API. In this study, transfer learning was carried out utilizing Faster RCNN (Region Based Convolutional Neural Networks) with Inception V2 and ResNet50 model weights. The accuracy of both trained models was the same, while Faster RCNN Inception V2 had the lowest loss (0.02).

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Telegram Bot for Emotion Recognition Using Acoustic Cues and Prosody 4th

Telegram Bot for Emotion Recognition Using Acoustic Cues and...

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4th International Conference on Computational Intelligence, Communications and Business Analytics, CICBA 2022

作者： Nag, Ishita Syed, Salman Azeez Basu, Shreya Shaw, Suvra Banik, Barnali Gupta Department of Computer Science and Engineering Institute of Engineering and Management Kolkata India Department of Information Technology Netaji Subhas University of Technology New Delhi India Department of Computer Science and Engineering University of Engineering and Management Kolkata India University of Hyderabad Campus Telangana Hyderabad500 046 India

ISBN: (纸本)9783031107658

In recent times, voice emotional recognition has been established as a significant field in Human computer Interaction (HCI). Human emotions are intrinsically expressed by linguistic (verbal content) information and paralinguistic information such as tone, emotional state, expressions and gestures which form the relevant basis for emotional analysis. Accounting the user’s affective state in HCI systems is essential to detect subtle user behaviour changes through which the computer can initiate interactions instead of simply responding to user commands. This paper aims to tackle existing problems in speech emotion recognition (SER) by taking into account the acoustic cues and prosodic parameters to detect user emotion. Here, the work will be mainly on the Ryerson Audio-Visual Database (RAVDESS) to extract Mel Frequency Cepstral Coefficients (MFCC) from signals to recognise emotion. The librosa library will be used for processing and extracting audio files before testing and classification is carried out using 4 different classifiers for a comparative study. This SER model is then integrated into a Telegram Bot to develop an intuitive, user-friendly interface as an application for psychological therapy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Emotion Recognition

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Privacy Protection of Biometric Templates Using Deep Learning 1

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International Conference on Innovations in Cyber Physical Systems, ICICPS 2020

作者： Arora, Shefali Bhatia, M.P.S. Kukreja, Harshita Jain, Soumya Division of Computer Engineering Netaji Subhas Institiute of Technology Delhi India Deparment of Computer Engineering Indira Gandhi Delhi Technical University for Women Delhi India

ISBN: (数字)9789811641497

ISBN: (纸本)9789811641480

With the growing use of biometric authentication in real-world applications, there are growing concerns related to the privacy of biometric templates enrolled in the database. Many algorithms have been proposed for the protection of biometric templates, however, this leads to a trade-off between the security of templates and matching performance. In this work, we propose a robust framework for improving upon the existing biometric template protection techniques. The proposed method uses one-shot enrollment for the mapping of images against unique binary codes assigned to them. The binary codes are obtained by binarization of image pixels. This is followed by the use of the SHA-256 hash function, and the class labels are generated based on the hashes templates. This is followed by the extraction of features using a deep convolutional architecture. During verification, the predicted labels are matched against labels generated from the hashed templates of the users stored during enrollment. The proposed framework is capable of achieving state-of-the-art performance. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Hash functions

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Deepfake Detection: Leveraging InceptionResNetV2 and LSTM for Enhanced Accuracy

Deepfake Detection: Leveraging InceptionResNetV2 and LSTM fo...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Aradhna Saini Attiuttama Sheenam Naaz Anushka Shivhare Gaurav Dhuriya Neha Yadav Computer Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida Artificial Intelligence and Machine Learning G L Bajaj Institute of Technology and Management Greater Noida Computer Science and Engineering Sharda University Greater Noida Computer Science and Engineering Netaji Subhas University of Technology Delhi

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The antique adage "seeing is believing" is simply no longer actual, which has profound implications for a ramification of sides of our lives. Deepfakes are becoming less complicated and simpler to create as technology advances. In fact, with the right software program, you can do a part of it right out of your palm. it is challenging to pick out deepfakes. The capability of the human eye to differentiate among deepfakes has reduced. however, a few academics have tried to identify deepfake. Deepfakes are media that can be generated by synthetic intelligence (AI) using algorithms. Artificial intelligence algorithms are designed to understand the traits of both the supply and the target photo. Subsequently, the target image is positioned on the pinnacle of the supply photograph. Our goal is to pick out video deepfakes by way of utilizing deep getting-to-know neural networks which include InceptionResNetV2 and LSTM. With the usage of transfer learning and the pre-trained InceptionResNetV2 CNN for characteristic extraction and vector production, we were able to efficaciously assemble a deepfake detection version. The capabilities had been used to train the LSTM layer, and the confusion matrix that was produced offers us the accuracy of testing and validation. The corresponding version's accuracy for 20 and 40 epochs was 84.75% and 91.48%, respectively.

关键词： Deepfakes Accuracy Neural networks Transfer learning Streaming media Vectors Artificial intelligence Long short term memory Faces Testing

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Unique analysis approach to bridge-t network using floating admittance matrix method

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International Journal of Circuits, Systems and Signal Processing 2021年 15卷 1297-1304页

作者： Roy, Sanjay Kumar Sharma, Kamal Kumar Bhargava, Cherry Singh, Brahmadeo Prasad OMQ Division TATA Steel Ltd India Electronics & Communication Engineering Lovely Professional University Phagwara Punjab India Symbiosis Institute of Technology Pune India Netaji Subhas University of Technology New Delhi India

The RC bridge-T Circuit are sometimes preferred for radio frequency applications as it does not require transformer (inductive coupling). The uses of the resistance-capacitance form of the network permits a wide tuning range. The article aims to develop a band pass filter's mathematical model using the Floating Admittance Matrix (FAM) approach. Both types of RC bridge-T network form the band-pass filters. The use of the conventional methods of analysis such as KCL, KVL, Thevenin's, Norton's depends on its suitability for the type of the particular circuit. The proposed mathematical modeling scheme using the floating admittance matrix approach is unique, and the same can be used for all types of circuits. This method is suitable to use the partitioning technique for large network. The sum property of all the elements of any row or any column equal to zero provides the assurance to proceed further for analysis or re-observe the very first equation. This saves time and energy. The FAM method presented here is so simple that anybody with slight knowledge of electronics but understating the matrix maneuvering, can analyze any circuit to derive all types of its transfer functions. The mathematical modeling using the FAM approach provides leverage to the designer to comfortably adjust their design at any stage of analysis. These statements provide compelling reasons for the adoption of the proposed process and demonstrate its benefits. The theoretically obtained equations meet the expected result for the RC bridge-T network. Its response peaks at the theoretically obtained value of the frequency. The simulated results are in agreement with the topological explanations and expectations. © 2021, North Atlantic University Union NAUN. All rights reserved.

关键词： Bandpass filters

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Effect of temperature and strain rate on mechanical properties and potential energy of nanocrystalline tantalum

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Materials Today: Proceedings 2022年 82卷 22-28页

作者： Kumar Gupta M. Panwar V. Prasad Mahapatra R. Department of Mechanical Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi-NCR Campus Modinagar U P Ghaziabad India Mechanical Engineering Department Netaji Subhas University of Technology Dwarka New Delhi 110078 India Department of Computer Science & Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi-NCR Campus Modinagar U P Ghaziabad India

In this work, molecular dynamics simulations have been conducted on nanocrystalline tantalum. Uniaxial tensile tests have been conducted and the effects of temperature and strain rate on mechanical properties and potential energy of nanocrystalline tantalum have been observed. The response of nanocrystalline tantalum with temperature variation from 300 K to 1500 K and step-size of 300 K at a constant strain rate has been analyzed. The response at different strain-rates i.e., 5 × 108 sec-1, 1 × 109 sec-1, 5 × 109 sec-1, 1 × 1010 sec-1 at constant temperature has also been studied. It has been observed that the yield strength and young's modulus show a drop of 14.4 % and 12.3 % respectively when the temperature is increased from 300 K to 1500 K, but increase by 105.8 % and 4.08 % respectively, when strain rate is increased from 5 × 108 sec-1 to 1 × 1010 sec-1. Moreover, it has been observed that the potential energy of nanocrystalline tantalum first increases in magnitude with an increase in strain, and then attains saturation at a constant value. The magnitude of this saturation potential energy has an inverse relationship with both temperature and strain rate. © 2022

关键词： Energy Mechanical properties Nanomaterials Strain rate Tantalum Temperature

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Asymmetric Dual-Hop NOMA based Communication System with Practical Constraints: Imperfect CSI, HWI and ISIC

Asymmetric Dual-Hop NOMA based Communication System with Pra...

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International Conference on Signal Processing and Communication (ICSC)

作者： Isha Khirwar Akash Gupta Bharat Verma Parul Garg ECE Department The LNM Institute of Information Technology Jaipur India Division of Electronics and Communication Engineering Netaji Subhas University of Technology New Delhi India

In this paper, we present an asymmetric dualhop decode-and-forward relay-based Non-Orthogonal Multiple Access (NOMA) based communication system. The source-to-relay link is characterized by $\eta-\mu$ distribution, while the relay to end users is characterized by Rayleigh distribution. Further, we have incorporated hardware impairments (HWI), and channel state information (CSI) errors due to imperfect successive interference cancellation (SIC), at the relay and end users. The mathematical framework for outage probability (OP) is being derived, and the analytical results are verified using Monte Carlo simulation results.

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