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5th IEEE International Conference on Computing Communication and Automation, ICCCA 2020

作者： Chakraborty, Pinaki Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9781728163246

Social networking sites became popular in the early-2000s and now have deep impact on the lives of their users. Facebook is the most popular social networking site today and is frequented by many high school and university students. I conducted this techno-behavioral study to understand the effects of using Facebook on the academic performance of students. I reviewed the diverse, and at times contradictory, results of 22 empirical studies carried out in 12 different countries in the last ten years. I observed that students use Facebook primarily for entertainment and communicating with people with similar interests. Using Facebook during class hours and spending too much time using Facebook negatively affect the grades of students. Nevertheless, some students also use Facebook to exchange educational resources and discuss academic issues with peers and instructors. Some instructors also improvise Facebook groups as learning management systems and use them to teach their courses. My findings will be useful to educational policy makers and social networking site developers. © 2020 IEEE.

关键词： Students

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Strategic Sleeping of Visible Light Communication Access Point 6

Strategic Sleeping of Visible Light Communication Access Poi...

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6th IEEE International Conference on Signal Processing, Computing and Control, ISPCC 2021

作者： Kumar, Vishal Gupta, Akash Garg, Parul Netaji Subhas University Of Technology Sector-3 Department Of Electronics And Communication Engineering Delhi Dwarka110078 India LNM Institute Of Information Technology Department Of Electronics And Communication Engineering Rajashthan Jaipur302031 India

ISBN: (纸本)9781665425520

Energy conservation is always an important performance characteristic of any technology. The deployment of strategic sleep scheme in Visible light communication (VLC) system can prevent energy wastage. The pilot signal is efficiently utilized for switching between active and sleep states of VLC access points (APs). The results demonstrate the energy efficiency of the proposed scheme over always ON VLC system. The scheme is being utilized for multiuser scenario and idle probability of AP is analysed. Further, the effect of false activation due to unwanted signal is demonstrated through results. © 2021 IEEE.

关键词： Energy efficiency

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Quantum Cryptography and Quantum Key 1

Quantum Cryptography and Quantum Key

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1st International Conference on Industrial Electronics Research and Applications, ICIERA 2021

作者： Sehgal, Shravan Kumar Gupta, Rashmi Ambedkar Institute of Advanced Communication Technologies and Research Department of Electronics and Communication Engineering Delhi India Netaji Subhas University of Technology Department of Electronics and Communication Engineering East Campus Delhi India

ISBN: (纸本)9781665435420

the area of Quantum Cryptography is a new and upcoming field in terms of security of data. Unlike the normal Cryptography techniques this technique is faster and also can handle large amount of data as it works on qubits and on the principle of Heisenberg Uncertainty. Quantum cryptography deals with the security applications mainly used in Quantum computers now a days. Furthermore, a security evaluation is given and any kind of attack that would present in any event a slip up speed of 46.875%. The paper represents various algorithm that are used in quantum cryptography along with its comparison with the existing classical cryptography algorithms. It also includes the shortcomings of the present algorithms and the future aspect of quantum cryptography. © 2021 IEEE.

关键词： VLSI circuits

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Multi-attributed and structured text-to-face synthesis

arXiv

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arXiv 2021年

作者： Wadhawan, Rohan Drall, Tanuj Singh, Shubham Chakraverty, Shampa Department of Computer Science and Engineering Netaji Subhas Institute of Technology New Delhi India

Generative Adversarial Networks (GANs) have revolutionized image synthesis through many applications like face generation, photograph editing, and image super-resolution. Image synthesis using GANs has predominantly been uni-modal, with few approaches that can synthesize images from text or other data modes. Text-to-image synthesis, especially text-to-face synthesis, has promising use cases of robust face-generation from eye witness accounts and augmentation of the reading experience with visual cues. However, only a couple of datasets provide consolidated face data and textual descriptions for text-to-face synthesis. Moreover, these textual annotations are less extensive and descriptive, which reduces the diversity of faces generated from it. This paper empirically proves that increasing the number of facial attributes in each textual description helps GANs generate more diverse and real-looking faces. To prove this, we propose a new methodology that focuses on using structured textual descriptions. We also consolidate a Multi-Attributed and Structured Text-to-face (MAST) dataset consisting of high-quality images with structured textual annotations and make it available to researchers to experiment and build upon. Lastly, we report benchmark Fréchet's Inception Distance (FID), Facial Semantic Similarity (FSS), and Facial Semantic Distance (FSD) scores for the MAST dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Timely Sensing of Forest Fire Using Ml Based Regressors

SSRN

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SSRN 2023年

作者： Arya, Archie Soni, Daanish Kabir Gupta, Megha Sharma, Deepak Kumar Department of Electrical Engineering Netaji Subhas University of Technology New Delhi India Jagan Institute of Management Studies Japan Department of Information Technology Indira Gandhi Delhi Technical University for Women Delhi India

Forests are crucial for the ecosystem, providing habitats for diverse species, regulating the climate, managing water, preventing soil erosion, and providing resources. Forest fires cause damage to the environment and human communities, emitting harmful pollutants and exacerbating climate change. Fire detection systems are essential for the early detection and prevention of fires, with various types of sensors used depending on the buildings size and use. This research paper aims to provide an outline of fire detection technology and suggest an improved approach. The proposed method uses the MODIS South America dataset to predict Fire Radiative Power in MW. The dataset is imported as a CSV file, converted to a data frame using Pandas Library, and categorical variables are numerically encoded. Outlier detection uses Interquartile Range and Z-score methods, and data is scaled using Standard Scaler from the Sklearn library. The result metrics (above 98% R2 score and more) show that the proposed method can be viable for predicting wildfires. © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Advancements in fabrication, polymorph diversity, heterostructure excitation dynamics, and multifunctional applications of leading 2D-transition metal dichalcogenides

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Journal of Industrial and engineering Chemistry 2025年

作者： Dhingra, Anuja Kumar, Rajesh Thakur, O.P. Pandey, Raghvendra Materials Analysis and Research Lab Department of Physics Netaji Subhas University of Technology New Delhi110078 India Green Energy Physics & Solid State Ionics Lab Department of Physics ARSD College University of Delhi New Delhi110021 India Department of Mechanical Engineering Indian Institute of Technology Uttar Pradesh Kanpur208016 India

Two-dimensional transition metal dichalcogenides (2D TMDs) have recently gained much attention due to their fascinating properties such as atomic thickness, direct-bandgap, enhanced mechanical strength, improved electronic behaviour, and widespread use in a range of exciting technologies such as energy storage, sensing, biomedical, nanoelectronics, photonics, opto-electronics, and photocatalysis. 2D TMDs exhibit diverse properties such as semiconducting (MoS2), semimetals (WTe2), metals (VSe2), and superconductors (NbSe2) which depends on their chemical compositions and structural phases. Moreover, different crystalline structure and stacking sequence also tailor the properties of 2D TMDs. This review article primarily discusses the structural aspects of prominent 2D TMDs, such as MoS2, MoSe2, WS2, WSe2, & MoTe2, and their defect engineering. The progress in various synthesis methods, such as chemical vapour deposition, exfoliation methods, and molecular beam epitaxy, along with their correlation with the phase engineering is also discussed. Further, emphasis on fundamental aspects of the ultrafast charge carrier dynamics in the different 2D TMD heterostructures and the mechanism behind the formation of interlayer excitons, which is crucial to govern the devices based on TMDs, is also addressed. A systematic discussion on the multifunctional applications and significant work undertaken to utilize these materials in a number of fields is also highlighted. © 2025 The Korean Society of Industrial and engineering Chemistry

关键词： Molecular beam epitaxy

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Deep and Machine Learning for monitoring groundwater levels and hydrological changes using GRACE and SENTINEL-1 for the Ganga River basin

Deep and Machine Learning for monitoring groundwater levels ...

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Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International Conference on

作者： Abhinav Galodha Kayithi Naga Sai Brejesh Lall Shaikh Ziauddin Ahammad Sanya Anees School of Interdisciplinary Research (SIRe) Indian Institute of Technology IIT Delhi New Delhi India Bachelor of Engineering Department of Mechanical Engineering Indian Institute of Technology IIT Delhi New Delhi India Professor Department of Electrical Engineering Indian Institute of Technology IIT Delhi New Delhi India Professor Department of Biochemical Engineering and Biotechnology Indian Institute of Technology IIT Delhi New Delhi India Assistant Professor Department of Electronics and Communication Engineering Netaji Subhas University of Technology New Delhi India

ISBN: (数字)9798350389678

ISBN: (纸本)9798350389685

Accurate estimating groundwater levels in river basins is paramount for hydro-geological research and sustainable water resource management. This research presents a deep-learning approach for accurate groundwater level estimates in the Ganga River Basin. Our method leverages the transformative potential of Vision Transformers (ViT) and their variants, with an emphasis on the Swin-Transformer variant enhanced with Normalization Attention Modules (NAMs), by utilizing the combined band information of Sentinel-1 synthetic aperture radar (SAR) and GRACE satellite data. We have assembled an extensive dataset and trained with computer vision to tackle the Ganga River basin’s particular difficulties. Cutting-edge Vision Transformers showed great promise in groundwater level estimation, with the model based on Swin-Transformer NAM achieving an exceptional Mean Absolute Error (MAE) of 1.2. The basin and beyond will benefit greatly from this study’s significant advancement of sustainable groundwater level and utilization practices.

关键词： Visualization Satellites Machine learning Transformers Rivers Synthetic aperture radar Monitoring

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Drug Traceability and Health Monitoring System with Hyperledger Fabric 6th

Drug Traceability and Health Monitoring System with Hyperled...

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6th International Conference on Blockchain and Trustworthy Systems, BlockSys 2024

作者： Vijayakumar, P. Rajkumar, S.C. Sivaraman, Audithan Jayarekha, P. Chandra sekaran, S. Department of Computer Science and Engineering University College of Engineering Tindivanam Villupuram Tamil Nadu Tindivanam604001 India Department of Computer Science and Engineering Anna University Regional Campus Madurai Keelakuilkudi Tamil Nadu Madurai625019 India Department of Information Technology Netaji Subhas University of Technology Jafferpur110073 India Department of Information Science and Engineering B.M.S. College of Engineering Karnataka Bangalore560019 India Department of Computer Science and Engineering P.S.V. College of Engineering and Technology Tamil Nadu Krishnagiri635108 India

ISBN: (纸本)9789819614134

The proposed system aims to transform drug traceability and patient authentication through the seamless integration of Hyperledger Fabric. This integration is designed to enhance transparency, security, and efficiency within the pharmaceutical supply chain. The system establishes a resilient framework that enables various stakeholders, including manufacturers and patients, to engage with a distributed ledger system powered by Hyperledger Fabric. Through the utilization of QR code technology and biometric authentication, the system ensures the legitimacy of medications and validates the identities of patients, thereby promoting trust and responsibility in healthcare transactions. Performance evaluation indicates that the proposed system exhibits an 85% increase in efficiency compared to current systems, providing improved transaction processing capacity, reduced latency, enhanced scalability, and heightened security measures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed ledger

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Transforming Hospital Visitor Screening with Cloud-Powered Deep Learning for Safety and Security

Transforming Hospital Visitor Screening with Cloud-Powered D...

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IEEE Students' Conference on Electrical, Electronics and computer Science (SCEECS)

作者： D. Antony Joseph Rajan Sivanesan T M N. Mohankumar M. Srinivasan Audithan Sivaraman S. Velmurugan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of Information Technology P. S. V College of Engineering and Technology Krishnagiri Tamilnadu Department of Information Technology Netaji Subhas University of Technology (Govt of NCTDelhi) Jafferpur New Delhi Department of Electronics and Communication Engineering T.J.S. Engineering College Tamil Nadu India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

This research describes a novel approach to hospital visitor screening utilizing cloud-powered deep learning using convolutional neural networks (CNNs). Our proposed approach will fill this need by improving threat detection accuracy and decreasing false positives. Our system can successfully detect possible dangers among visitors by using CNNs, renowned for their ability to perform image recognition tasks. Integrating cloud computing also makes scalability, real-time processing, and centralized administration possible across several hospital locations. This rigorous testing and evaluation technique improves hospital security requirements reliably and resiliently. It presents novel methods to transform visitor screening fundamentally, solving the challenge of keeping patients, staff, and facilities secure from evolving dangers. The proposed method greatly improves hospital security since it might increase safety and reduce dangers.

关键词： Deep learning Cloud computing Image recognition Accuracy Hospitals Threat assessment Real-time systems Security Convolutional neural networks Testing

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Mathematical analysis of the effect of process parameters on angular distortion of MIG welded stainless steel 202 plates by using the technique of response surface Methodology

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Materials Today: Proceedings 2021年 41卷 1045-1054页

作者： Mayank Pandit Shruti Sood Prithu Mishra Pradeep Khanna Department of Manufacturing Processes and Automation Engineering Netaji Subhas Institute of Technology New Delhi 110078 India

Gas Metal Arc Welding (GMAW) is a widely used semi-automated joining process used in metal fabrication and for producing good quality welds with high deposition rate. The process is versatile because of its ability to weld in all positions and the suitability of wide range of metals and alloys with compatible filler wire to get welded. The process further can be fully automated by using programmable robots thus making it suitable for mass production. Like other fusion welding processes, because of rapid heating and cooling cycles, various types of distortions are encountered, which are detrimental to the performance of the weld. Out of various kinds of distortions, the angular one is found to be prominent. This distortion once occurred require considerate amount of post-welding rectifications and creates design related issues because of the mismatch in the assembly. Therefore, the endeavour should be to keep this distortion minimum by selecting the weld parameters optimally. The present investigative work aims to study the effect of different individually controllable input parameters like wire feed rate, welding speed, voltage, standoff distance and torch angle on the resulting angular distortion of Stainless Steel 202 plates. SS 202 is manganese alloyed austenitic stainless steel, which is designed as a cost-effective alternative to 304 grades with similar corrosion and mechanical properties at lower temperatures and less harsh corrosive environment. By the use of statistical approach, an attempt has been made to develop a mathematical model that relates these input parameters to the response parameter which is angular distortion. This model would prove to be helpful in predicting the resulting angular distortion at different combinations of input parameters. Statistical technique of central composite face centred design has been used to develop the model and ANOVA is used to check the adequacy of the same. Response Surface Methodology (RSM) has been used to analy

关键词： Stainless steel 202 GMAW Angular distortion Mathematical modelling ANOVA RSM

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