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Novel concept of tailorable magnetic field and electron pressure distribution in a magnetic nozzle for effective space propulsion

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Propulsion and Power Research 2023年第1期12卷 59-68页

作者： Lohit Malik Department of Manufacturing Processes and Automation Engineering Netaji Subhas University of Technology(formerly Netaji Subhas Institute of Technology)New DelhiIndia Department of Mechanical and Aerospace Engineering Princeton UniversityPrincetonNJ 08540United States

Magnetic nozzle appears to be a practical solution for prolonged space *** efficient handling of the spaceship,an in-flight solution to customize the thrust from the magnetic nozzle is *** a new concept of three-thick coils system is proposed for tailoring the magnetic field in-flight in accordance with electron pressure *** role of peak position of the pressure and its axial gradient is also uncovered for realizing higher *** three-fold increase in thrust is observed when the electron temperature is raised to w2.5 times of its original value at the exit *** set-up is optimized for its best performance and efficient use in the electric space propulsion sector with thrust approaching 5 *** particular,this can contribute to the attitude control or the precision pointing of the spacecraft,the technology for removal of space debris and manipulating the ion momentum flux lost to a wall or unsteady laser produced plasma flow in a magnetic nozzle.

关键词： Tailorable magnetic field Magnetic nozzle Electron pressure Thrust Effective space propulsion

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Levy Flight Based Fire Hawk Optimizer

Levy Flight Based Fire Hawk Optimizer

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Baweja, Daksh Jain, Archit Bhandari, Aditya Bohat, Vijay Kumar Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350364866

This paper introduces the Levy Flight based Fire Hawk Optimizer (LFHO), an enhanced variant of the Fire Hawk Optimizer (FHO). The idea for LFHO emerged from observing the limitations in the original FHO, notably its re-stricted exploration capability and its tendency to get trapped in local optima, leading to sub-optimal solutions. Our motivation was to overcome these limitations and enhance the algorithm's performance in multifaceted problems. LFHO incorporates a random walk mechanism to inject stochasticity into the search process, thus broadening the exploration scope and mitigating premature convergence issues. Additionally, the introduction of dual global best solutions facilitates a more comprehensive exploration of the solution space, while a dynamic re-initialization mechanism for global best solutions ensures adaptability to evolving search landscapes. Furthermore, a refined nearest function enhances navigation towards promising solutions with greater accuracy and efficiency. The LFHO algorithm's performance is evaluated on CEC(Congress on Evolutionary Computation) 2014 benchmark functions, demonstrating significant improvements over the original FHO in addressing complex optimization tasks. © 2024 IEEE.

关键词： Stochastic systems

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Identifying digital biomarkers in actigraph based sequential motor activity data for assessment of depression: a model evaluating SVM in LSTM extracted feature space

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International Journal of Information technology (Singapore) 2023年第2期15卷 797-802页

作者： Arora, Anshika Chakraborty, Pinaki Bhatia, M.P.S. Department of Computer Science and Engineering Netaji Subhas University of Technology New Delhi India

This research puts forward a methodology for depression assessment using actigraph recordings of motor activity. High level features of motor activity are extracted using Long-Short Term Memory (LSTM) which are paired with statistical features to deliver valuable digital biomarkers. Overlapping sliding window is used to input sequences into LSTM to capture superior features in activity recordings. The predictive ability of these digital biomarkers is evaluated using Support Vector Machine (SVM). The hybrid framework is validated on benchmark, Depresjon dataset and achieves accuracy of 95.57%. Effectiveness of overlapping sliding window and statistical features is evaluated, and their significance is validated. It is validated that the concept of overlapping sliding window improves performance accuracy by 3.51% and the use of discriminative statistical features improves model performance by 1.08%. It is concluded that the proposed methodology based on feature extraction, statistical features and overlapping sliding window outperforms state-of-the-art techniques as well as baseline deep learning algorithms for depression detection. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Actigraph Depression Digital biomarkers LSTM Motor activity SVM

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A BERT-BiLSTM Approach for Socio-political News Detection 1

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Singh, Pratima Jain, Amita Department of Computer Science Engineering Netaji Subhas University of Technology New Delhi110031 India

ISBN: (数字)9789819760367

ISBN: (纸本)9789819760350

The detection of socio-political news is critical for ensuring informed citizens, holding media accountable, understanding public sentiment, detecting misinformation, guiding policy development, monitoring crises, facilitating international relations, and ensuring public safety. In this paper, a novel model is proposed as the BERT-based BiLSTM (Bidirectional Encoder Representations from Transformers–Bidirectional Long Short-Term Memory) model to balance the accuracy with computational efficiency and detect socio-political news. To begin, the BERT model is used instead of the traditional word embedding model to represent the word vector. The text representation is calculated based on the contextual information, and it is adjusted according to the sentiment of the word while the contextual information is fused. Second, the BiLSTM model is linked to the BERT model, allowing the BiLSTM model to extract socio-political news features from both directions at the same time. The proposed model’s performance is evaluated and compared with stats of art methods. In the comparison, the BERT-BiLSTM model detects socio-political news with an accuracy of 96.8%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Public policy

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Phoneme Based Hindi Speech Recognition Using Deep Learning 2

Phoneme Based Hindi Speech Recognition Using Deep Learning

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Singh, Shivangi Bhatt, Shobha Netaji Subhas University of Technology Department of Computer Science and Engineering East Campus Delhi India

ISBN: (纸本)9798350371055

A phoneme represents the smallest distinct sound unit within a language, unique to each linguistic system. Phoneme identification finds applications in various speech-related technologies, including automatic speech recognition, language learning, assistive technology for speech impaired individuals. The realm of Automatic Speech Recognition (ASR) stands as a formidable challenge within the field, with a predominant emphasis on extensively spoken languages. However, the intricacies posed by Hindi speech recognition have received limited attention, owing to the language's complex structure and the scarcity of pertinent data. Existing models designed for languages like English cannot be seamlessly adapted to the nuances of Hindi. This study presents an approach to phoneme-based speech recognition in the Hindi language through the implementation of a deep learning model. The proposed model leverages techniques in ASR to transform raw audio data into phoneme sequences, assisting in recognizing digits from a dataset collected in limited capacity consisting of counting in Hindi language. © 2024 IEEE.

关键词： bi-LSTM (Bi-directional Long Short-Term Memory) Convolutional Neural Network (CNN) Hindi speech recognition MFCC

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Enhancing Abstractive Text Summarisation Using Seq2Seq Models: A Context-Aware Approach

Enhancing Abstractive Text Summarisation Using Seq2Seq Model...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Kumar, Nikesh Kumar, Suresh Netaji Subhas University of Technology Department of Computer Science & Engineering East Campus Delhi India

ISBN: (纸本)9798350382723

This study introduces a novel approach to abstractive text summarisation, by harnessing the power of sequence-to-sequence (Seq2Seq) models along with word sense disambiguation (WSD) techniques. We utilised TensorFlow and NLTK libraries, employing the CNN/Daily Mail dataset to train the model. We integrated word sense disambiguation [17] in the preprocessing phase. With the help of WordNet we identified and utilised most common senses of word within texts. This step played a crucial role in improving the model's *** created a Seq2Seq model equipped with LSTM units, embedding layers, and Time Distributed Dense layers, while focusing on optimising the vocabulary size, embedding dimensions, and LSTM units to achieve efficient summarisation. The model is trained on the preprocessed dataset, undergoing numerous epochs to refine its summarisation *** approach shows a significant improvement in capturing the context and nuances of the input texts, resulting in more coherent and accurate summaries. The results indicate promising advances in the field of abstractive text summarisation, opening ways for context-aware and semantically rich summarisation tools. This study not only contributes to the academic discussions in natural language processing but also offers practical implications for summarisation applications in various domains. © 2024 IEEE.

关键词： Embeddings

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Quantum Machine Learning: A Comprehensive Overview and Analysis 15

Quantum Machine Learning: A Comprehensive Overview and Analy...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Misra, Shreshtha Rani, Poonam Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi110078 India

ISBN: (纸本)9798350370249

The emerging field of Quantum Machine learning (QML), which offers noticeably faster processing speed, is the result of the merger of Quantum Computing (QC) and Machine Learning (ML). Superposition and Entanglement are the fundamental concepts of QC, which increases computational scalability. This comprehensive overview examines QML foundations, algorithms, applications, and challenges of QML. When applied to the Cleveland Heart Disease Dataset, the Quantum Support Vector Classifier (QSVC) achieves an accuracy of 0.75 whereas the Variational Quantum Classifier (VQC) yields an accuracy of 0.62, thus showing that QSVC outperforms VQC. The main issue in QML that calls for effort and investment is noise. QML has the potential to advance technology and has its application in a variety of domains. © 2024 IEEE.

关键词： Entanglement QML QSVC Superposition VQC

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Choice: AI Assisted Intelligent Goal Tracking 6th

Choice: AI Assisted Intelligent Goal Tracking

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

作者： Gupta, Harshit Jha, Saurav Handa, Shrija Gairola, Tanmay Department of Computer Science and Engineering Netaji Subhas University of Technology New Delhi110078 India

ISBN: (纸本)9789819940707

It is often the case that people set up elaborate goals but are not able to fully implement them. A "goal" is defined as the progress or familiarity with a topic in which the user wants to excel. There are applications which allow people to make and track goals and their progress in a particular domain, but are heavily dependent on the user’s input. Only the user knows whether they have completed their goal or not and the application is just reflecting what the user already knows. With Choice, a system would be introduced which not only lets people set their goals, but also monitors whether they are indeed sticking to them. The basis of the application is history distribution of a user. The application uses anonymous history tracking and Machine Learning to provide insights into search patterns, thus enabling better analysis of search topics for users. It is specifically targeted toward university students and can be coupled with a customizable chat-bot to further provide relevant and helpful information to the students. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Machine learning

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FreqEmo: A Twitter Dataset Comprising Users' Tweeting Frequency and Emotions

FreqEmo: A Twitter Dataset Comprising Users' Tweeting Freque...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Agrawal, Aryan Arora, Anshika Chakraborty, Pinaki Netaji Subhas University of Technology Department of Electronics and Communication Engineering New Delhi India School of Computer Science Engineering and Technology Bennett University Greater Noida India Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350379716

Social media is now indispensable, used by all social, economic, age, and demographic groups. It provides extensive user-generated data, offering insights into users' mental states. This paper introduces a novel Twitter dataset comprising users' tweeting frequency and emotions shared. The dataset is based on Tweets collected from the USA and India during a 60-day period using adjectives representing emotions. Further, for frequently tweeting users, NRC emotion lexicon has been implemented which extracted sentiment and emotion scores of the users. The final dataset includes users' tweeting patterns along with the distribution of emotions and sentiments in their tweets. This dataset has been made publicly available and this project aims to help researchers develop models to assess users' emotional states based on social media activity without self-reporting, with applications in correlating usage patterns with well-being, clustering users by emotions and tweeting patterns, and analyzing temporal, spatial, and demographic trends. To provide baseline performance, k-means clustering is implemented in this study to cluster the users with similar usage patterns. Clusters are analyzed to determine the types of emotions and sentiments shared by users within each cluster. Based on the analysis, it is concluded that as Twitter usage increases within the clusters, the proportion of users dominated by negative sentiment also rises significantly, peaking at 41.67%. Additionally, the percentage of users whose predominant emotion is joy decreases with increased usage, reaching a low of 8.33% as opposed to the peak value of 69.44% in the group characterized by least usage, while the percentage of users dominated by anger and disgust grows to 17.24% and 4.17% as their usage intensifies. © 2024 IEEE.

关键词： Tweets

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Biodiesel yield optimisation from a third-generation feedstock (microalgae spirulina) using a hybrid statistical approach

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International Journal of Ambient Energy 2023年第1期44卷 1202-1213页

作者： Ahmad, Aqueel Yadav, Ashok Kumar Singh, Achhaibar Mechanical Engineering Department Netaji Subhas University of Technology New Delhi India Department of Mechanical Engineering Raj Kumar Goel Institute of Technology Ghaziabad India

The current study aims to find the optimum input parameters for high-quality biodiesel synthesis utilising response surface methodology (RSM), a genetic algorithm (GA), and the cuckoo search algorithm (CSA) approach. The L30 pre-designed and executed experiments explored the significance of four process parameters: the methanol to oil molar ratio, catalyst concentration, reaction temperature, reaction time, and their combined effect on biodiesel production. The optimum conditions for biodiesel production were a molar ratio of 7.54:1 (methanol to oil), a KOH catalyst concentration of 0.5 wt. %, a reaction temperature of 65 °C for 102.57 min of reaction time, and a corresponding value of yield of 97.76%. With a correlation coefficient (R2) of 98.23 and a root-mean-squared error (RMSE) of 0.9995, it was seen that RSM gave a robust and consistent model. Microalgae methyl ester fuel characteristics were evaluated and compared to ASTM standards and found acceptable. Thus, the synthesis of high-quality, high-yield biodiesel from microalgae is a feasible alternative. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Microalgae

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