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2nd International Conference on Signals, machines, and Automation, SIGMA 2022

作者： Diwakar, Shivin Dwivedi, Dhirendra Singh, Satya P. Sharma, Madan Center of Excellence in Artificial Intelligence and Division of Electronics and Communication Netaji Subhas Institute of Technology University of Delhi New Delhi India Department of Information and Communication Engineering College of Engineering University of Buraimi Al Buraimi Governorate Oman

ISBN: (纸本)9789819909681

Analyzing human activity through wearable sensors can assist applications connected to context—vigilance and health care. The proposed approach utilizes convolutional and recurrent modeling to express the space–time-dependent constraints from information provided by numerous sensors. We construct a neural network layout, a combination of convolutional neural network and recurrent neural network, which focuses on spatiotemporal characteristics provided by sensor time series data with short listing and learning crucial points by executing a self-attentive procedure. We exhibit validation of our designed strategy on the WISDM dataset and indicate that the self-attention technique achieves a remarkable amendment in presentation over deep neural networks through the union consisting of convolutional and recurrent network architectures. We also present that our designed algorithm offers a statistic performance improvement over other designed approaches for the information (dataset) under consideration. The presented technique allows precise interpretation of activity from numerous body part sensors through various time intervals. We evaluated our technique through various classification methods namely: MLP Classifier, gradient boosting, Random Forest Classifier, Conv2D–LSTM, and self-attention mechanism (proposed). The simulation results show that our proposed method achieved the highest accuracy of 89.58% out of all other methods. We compared the effects of the designed architecture scheme with a baseline constructed through ConvNN and RecNN models, and with earlier designed approaches. We figured out that our self-attention model outperformed the baseline model with high difference among all the considered metrics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Wearable sensors

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Comparison of LSCP and Cluster Coverage Model for Emergency Train's Assets Management: Use Case;North Eastern Railway

Comparison of LSCP and Cluster Coverage Model for Emergency ...

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2022 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2022

作者： Mishra, Kshitij Mishra, Swastika Abhyuday International Institute of Information Technology Computer Engineering Hyderabad India Netaji Subhas University of Technology Mechanical Engineering New Delhi India Indian Institute of Technology Indian Railways Service of Mechanical Engineers Kharagpur India

ISBN: (纸本)9781665461795

Deployment of Accident Relief Medical Vans (ARMV) used for accident relief and rescue operations is generally done heuristically. ARMV is an ambulance-train equipped with various tools like jacks, heavy cutting tools, generators, operation theatre, artificial breathing equipment etc. to ease the rescue process. Taking North Eastern Railway (NER) as a case study, Location Set Covering Problem (LSCP) model will be used to find the minimum number of ARMVs required for the given network and their locations so that all demand points can be "covered"within a maximum of one hour @ 100 kmph for each of the nearest facility. This paper proposes a new mathematical model based on the Cluster Coverage Model (CCM) to provide a better coverage of ARMV's demands. We have used Girvan-Newman (GN) algorithm, a weight-based graph for clustering of NER network where nodes are taken as depots with ARMV parking facilities & crew availability and distance between these nodes is taken as edge weight of the graph. Finally, we present a comparison of results between the proposed cluster coverage models with LSCP and suggest relocations of the existing ARMVs and show that our proposed model is economical and provides a better coverage of ARMVs demands. © 2022 IEEE.

关键词： Location

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Video Frame Deletion Detection using Correlation Coefficients 8

Video Frame Deletion Detection using Correlation Coefficient...

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8th International Conference on Signal Processing and Integrated Networks, SPIN 2021

作者： Singla, Neetu Singh, Jyotsna Nagpal, Sushama Department of Computer Engineering Netaji Subhas University of Technology New Delhi India Multimedia Research Lab Netaji Subhas University of Technology New Delhi India

ISBN: (纸本)9781665435642

In this paper, we propose feature-based machine learning models for detecting frame deletion tampering in videos. The work investigates inconsistency in correlations between adjacent frames that occurs when frames are dropped from a continuous sequence. As a result, the correlation pattern of the original and counterfeit videos differs slightly. Three machine learning models namely Support Vector Machine (SVM), Multilayer Perceptron (MLP) and Convolution Neural Network (CNN) have been implemented to predict the authenticity of video shots. Experiments have been conducted on a large dataset of 600 videos each of 25-frame deletion and 100-frame deletion. The results show that the CNN model can classify between authentic and forged sequences more accurately than SVM and MLP with the highest accuracy of 97% for 100-frame deletion. © 2021 IEEE

关键词： Support vector machines

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Air Writing: Tracking and Tracing 1

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5th International Conference on computer Vision and Image Processing, CVIP 2020

作者： Mall, Jyotsana Rani, Komal Khatri, Deepak Department of Computer Engineering Netaji Subhas University of Technology New Delhi110078 India Department of Biotechnology Netaji Subhas University of Technology New Delhi110078 India

ISBN: (数字)9789811611032

ISBN: (纸本)9789811611025

Writing is an integral part of learning. Its value is quintessential in the development of cognitive understanding which aids in absorbing and de-mystifying the world around us. Air writing as a concept has been prevalent in education systems all across the world;teachers and parents have been training children to gesture the shapes of letters and spell out words in the air to build the habit of writing, but its technology is relatively recent. In this paper, we have introduced a method to implement air writing which allows users to trace linguistic characters in a pen-up pen-down motion of finger or hand thereby eradicating the need to write continuously. The proposed method is not frustrated by the requirement of extra hardware like a data glove or a specialized camera. The implementation makes use of the in-built camera in personal computers, detects the hand region, and traces or tracks the hand trajectory as per the user’s requirement. The implementation is broadly divided into two parts: (i) Detection of hand region;and (ii) Tracking and tracing hand movements. We propose the use of Single Shot Multibox Detector (SSD) with MobilenetV1 as the base network to detect the hand region. For the second half of the implementation, we have used the deque data structure and OpenCV. We have also included the option of complete/partial erasing of the screen to simplify the process of air writing and make it more intuitive. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cameras

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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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Humor Generation and Detection in Code-Mixed Hindi-English

Humor Generation and Detection in Code-Mixed Hindi-English

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2021 Student Research Workshop, SRW 2021 associated with the International Conference on Recent Advances in Natural Language Processing, RANLP 2021

作者： Agarwal, Kaustubh Narula, Rhythm Department of Computer Science and Engineering Netaji Subhas University of Technology New Delhi India

ISBN: (纸本)9789544520762

Computational humor generation is one of the hardest tasks in natural language generation, especially in code-mixed languages. Existing research has shown that humor generation in English is a promising avenue. However, studies have shown that bilingual speakers often appreciate humor more in code-mixed languages with unexpected transitions and clever word play. In this study, we propose several methods for generating and detecting humor in code-mixed Hindi-English. Of the experimented approaches, an Attention Based BiDirectional LSTM with converting parts of text on a word2vec embedding gives the best results by generating 74.8% good jokes and IndicBERT used for detecting humor in code-mixed Hindi-English outperforms other humor detection methods with an accuracy of 96.98%. © 2021 Incoma Ltd. All rights reserved.

关键词： Long short-term memory

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A Comparative Study on Simulation Environment on Fog Computing 1

A Comparative Study on Simulation Environment on Fog Computi...

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1st IEEE International Conference on Emerging Trends in Industry 4.0, ETI 4.0 2021

作者： Kumar, Bijendra Jain, Vibha Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781665422376

The evolution of Information and Communication technology (ICT) has a massive impact on the growth of distributed systems such as the Internet of Things (IoT), cloud computing, and fog computing. All these technologies are closely coupled, where several IoT devices all together can generate high volume data that need to be processed either on cloud data centers or proximity deployed fog devices depending upon the nature of user application. Though these IoT-fog-cloud systems are very complex to design and implement, hence, to validate the flexibility and scalability of the new design, simulation tools areused. These days several types of fog simulators are available in the market depending upon the need of design, but it is very cumbersome task to select the best suitable tool. In this paper, a comparative study is present among nine available fog simulators which aim to help researchers and practitioners to select appropriate simulators based on their research work. © 2021 IEEE.

关键词： Fog

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Machine learning-guided humidity-induced degradation analysis of Cs2CuSbCl6 sustainable perovskite solar cell

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Renewable Energy 2025年 250卷

作者： Raj, Manasvi Batra, Sajal Aggarwal, Anshul Kushwaha, Aditya Goel, Neeraj Department of Electronics and Communication Engineering Netaji Subhas University of Technology Dwarka New Delhi110078 India Department of Computer Science & Engineering SRM University Delhi-NCR Haryana Sonepat131029 India

In this study, we present a machine learning (ML)-predicted framework to evaluate both the performance and environmental stability of a novel lead-free perovskite solar cell with the structure FTO/AZnO/Cs2CuSbCl6/MoO3. Our approach integrates theoretical calculations, device simulations, and ML to predict device behaviour under varied conditions. Density Functional Theory (DFT) calculations were employed to determine intrinsic electronic properties, yielding bandgaps of 3.481 eV for FTO, 3.330 eV for AZnO, 1.7 eV for Cs2CuSbCl6, and 3.170 eV for MoO3. Optical analysis confirmed that Cs2CuSbCl6 exhibits strong UV absorption with minimal losses, positioning it as an effective absorber. Subsequently, SCAPS-1D simulations optimized photovoltaic parameters, achieving an open-circuit voltage (Voc) of 1.58 V, a short-circuit current density (Jsc) of 21.82 mA/cm2, a fill factor (FF) of 91.12 %, and a power conversion efficiency (PCE) of 31.50 %. Nearly 100 % quantum efficiency in the visible spectrum further demonstrates efficient charge carrier extraction. To address moisture-induced degradation, we developed a linear regression ML model trained on both experimental and simulated data, which achieved an R2 of 0.989. The model reliably predicts that increased humidity and prolonged exposure significantly reduce PCE. This integrated methodology of DFT, device simulation, and ML offers a powerful tool for designing robust, high-performance perovskite photovoltaics with enhanced environmental resilience. © 2025 Elsevier Ltd

关键词： Open circuit voltage

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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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An Accurate Speech Emotion Analysis using Gradient Boosting and BiLSTM Techniques

An Accurate Speech Emotion Analysis using Gradient Boosting ...

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Contemporary Computing and Communications (InC4), IEEE International Conference on

作者： Ineesh Singh Raina Nikhil Gupta Gulshan Kumar Geetanjali Rathee Department of Computer Science Engineering Netaji Subhas University of Technology Main Campus New Delhi India

In the field of speech emotion recognition, machine learning (ML) algorithms face a challenging problem in recognizing emotional states. Speech emotion recognition (SER) is crucial in many real-time solutions, including human-robot interaction, assessing human behavior, and analyzing the emotional state of speakers. Due to its intricacy, SER is a task that is still difficult to complete but one that is becoming more and more crucial in emotional computing. Although there are many datasets available for SER, we are selecting RADVESS since we believe it to be the most ap-propriate. For many applications, such as a speech-to-speech translation system, feature selection can increase the accuracy of speech emotion recognition. Although tens of thousands of features can be retrieved from speech signals, it can be challenging to determine which elements are most closely associated to speaker emotional state. Most characteristics that are connected to emotional states are yet to be discovered. Mel Spectrogram and MFCC feature selection are the major topics of this research. Then passing these combined features through a modified CNN. In this paper we have used various neural network techniques (NN) for SER. The models we used include CNN, MLP, CNN-Attention-BiLSTM, Gradient Boosting. CNN-Attention- BiLSTM gives the best results among these models with accuracy of about 86 percent, precision of 0.834, recall of 0.802, F1-Score of 0.802.

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