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Hopping-mean: an augmentation method for motor activity data towards real-time depression diagnosis using machine learning

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Multimedia Tools and Applications 2024年 1-19页

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

The advances from the last few decades in the fields of ML (Machine Learning), DL (Deep Learning), and semantic computing are now changing the shape of the healthcare system. But, unlike physical health problems, diagnosis of mental health problems lacks a direct quantitative test. A huge variety of data generated by wearable IoMT (Internet of Medical Things) devices (e.g. wrist actigraph) provides an efficient objective way to diagnose such mental health problems in the early stages. In this paper, we propose a novel overall methodology comprised of preprocessing, data augmentation, and classification of depressive episodes from motor activity data. A time-efficient Augmentation Algorithm is proposed based on windowing over the same timestamps to resolve the problem of data loss in the Depresjon database and the UMAP (Uniform Manifold Approximation and Projection) based feature extraction method is used on motor activity database containing variable-length activity records for 23 control patients and 32 condition groups. The classification of depression from 24 h of motor activity data with augmentation performs much better than raw data. The preprocessing approach followed by Random forest provides cut over cut over the current state of the art and provides the highest accuracy of 74.29%, specificity of 74.80%, and sensitivity of 73.56 on non-augmented data and gives the highest accuracy of 98.65%, specificity of 97.36% and sensitivity of 99.89% on augmented data. This shows the effectiveness of using the custom augmentation approach proposed in this paper. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Medical problems

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Framework for Domain Independent Hybrid Recommender Systems: Addressing Challenges of Imperfect Data

Framework for Domain Independent Hybrid Recommender Systems:...

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2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Vibhute, Amey Netaji Subhas Institute of Technology Department of Computer Engineering Delhi India

ISBN: (纸本)9798350382693

Recommender systems are an indispensable aspectof numerous contemporary online platforms, aiding users inuncovering new and pertinent content. However, both content-basedfiltering (CBF) and collaborative filtering (CF) systemsexhibit distinct strengths and limitations. CBF systems typicallyexcel at addressing cold start issues but their recommendationsmay falter when metadata is incomplete or absent. CF systems,on the other hand, can provide superior recommendationquality when ample data is accessible, but they may fail wheninsufficient data is available. To mitigate these limitations, thispaper puts forward a hybrid recommender system that harnessesthe strengths of both CBF and CF methodologies. Our systemassigns confidence scores to both models and dynamically assignsweights based on those scores to generate the most advantageousrecommendations. This approach empowers us to circumventthe drawbacks of each model and capitalize on their respectivestrengths. Our framework has been evaluated on a vast dataset ofuser ratings and movie metadata. The findings demonstrate thatour hybrid system outshines both CBF and CF systems in termsof recommendation quality, particularly for cold start users andunpopular items. Notably, our hybrid system empowers users tofine-tune the balance between the two models using a handfulof parameters which allows users to tailor the recommendationengine to their specific data and preferences. © 2024 IEEE.

关键词： Matrix factorization

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Blockchain-based rumor detection approach for COVID-19

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 435-449页

作者： Rani, Poonam Jain, Vibha Shokeen, Jyoti Balyan, Arnav Department of Computer Engineering Netaji Subhas University of Technology Delhi India Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Haryana Rohtak India

The ubiquity of handheld devices and easy access to the Internet help users get easy and quick updates from social media. Generally, people share information with their friends and groups without inspecting the posts’ veracity, which causes false information propagation in the network. Moreover, detecting false news and rumors in such a massive load of unstructured information is a very tedious task. Results, many literature papers explored different machine learning and deep learning approaches to detect the presence of rumors on social media networks. Although detection of misleading news and rumors is not sufficient, therefore, we have proposed a model for the detection and prevention of transmitted rumors in this paper. In this paper, we use blockchain technology to verify the credibility of information and design a framework with four layers: network layer, blockchain layer, machine layer, and device layer, to prevent the propagation of rumors in the network. We also use deep learning techniques to identify the anomalies in the network. The Bi-directional Long Short Term Memory (Bi-LSTM) model is used to prevent the introduction of new rumors by continuously monitoring incoming messages in the network. The experimental results demonstrate that the proposed Bi-LSTM model outperforms state-of-the-art machine learning methods and recent baseline work. Performance is compared over different metrics such as accuracy, precision, recall, f1-score, and specificity. Experiment results show that our Bi-LSTM model outperforms all the other approaches and achieved 99.63 % accuracy. Additionally, the probability of incorrect detection is significantly low with only 0.13% false positive. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： COVID-19

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Landmark-Aware and Part-Based Ensemble Transfer Learning Network for Static Facial Expression Recognition from Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 349-361页

作者： Wadhawan, Rohan Gandhi, Tapan K. Netaji Subhas University of Technology Computer Science and Engineering Department Netaji Subhas Institute of Technology New Delhi110078 India Indian Institute of Technology Delhi Iit Campus Electrical Engineering Department New Delhi110016 India

Facial expression recognition from images is a challenging problem in computer vision applications. Convolutional neural network (CNN), the state-of-the-art method for various computer vision tasks, has had limited success in predicting expressions from faces having extreme poses, illumination, and occlusion conditions. To mitigate this issue, CNNs are often accompanied by techniques like transfer, multitask, or ensemble learning that provide high accuracy at the cost of increased computational complexity. In this article, the authors propose a part-based ensemble transfer learning network that models how humans recognize facial expressions by correlating visual patterns emanating from facial muscles' motor movements with a specific expression. The proposed network performs transfer learning from facial landmark localization to facial expression recognition. It consists of five subnetworks, and each subnetwork performs transfer learning from one of the five subsets of facial landmarks: eyebrows, eyes, nose, mouth, or jaw to expression classification. The network's performance is evaluated using the Cohn-Kanade (CK+), Japanese female facial expression (JAFFE), and static facial expressions in the wild datasets, and it outperforms the benchmark for CK+ and JAFFE datasets by 0.51% and 5.34%, respectively. Additionally, the proposed ensemble network consists of only 1.65 M model parameters, ensuring computational efficiency during training and real-time deployment. Gradient-weighted class activation mapping visualizations of the network reveal the complementary nature of its subnetworks, a key design parameter of an effective ensemble network. Lastly, cross-dataset evaluation results show that the the proposed ensemble has a high generalization capacity, making it suitable for real-world usage. © 2020 IEEE.

关键词： Face recognition

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Physics of 2D Materials for Developing Smart Devices

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Nano-Micro Letters 2025年第8期17卷 449-490页

作者： Neeraj Goel Rahul Kumar Department of Electronics and Communication Engineering Netaji Subhas University of TechnologyDwarkaNew Delhi 110078India Institute of Infrastructure Technology Research and Management Ahmedabad 380026India

Rapid industrialization advancements have grabbed worldwide attention to integrate a very large number of electronic components into a smaller space for performing multifunctional *** fulfill the growing computing demand state-of-the-art materials are required for substituting traditional silicon and metal oxide semiconductors ***-dimensional(2D)materials have shown their tremendous potential surpassing the limitations of conventional materials for developing smart *** their ground-breaking progress over the last two decades,systematic studies providing in-depth insights into the exciting physics of 2D materials are still ***,in this review,we discuss the importance of 2D materials in bridging the gap between conventional and advanced technologies due to their distinct statistical and quantum ***,the inherent properties of these materials could easily be tailored to meet the specific requirements of smart ***,we discuss the physics of various 2D materials enabling them to fabricate smart *** also shed light on promising opportunities in developing smart devices and identified the formidable challenges that need to be addressed.

关键词： 2D materials Heterostructures Smart devices Van der Waals Flexible electronics

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SAFARM: simulated annealing based framework for association rule mining

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International Journal of Information technology (Singapore) 2025年第3期17卷 1523-1532页

作者： Kaur, Preeti Goel, Sujal Tyagi, Aryan Malik, Sharil Shrivastava, Utkarsh Department of Computer Science and Engineering Netaji Subhas University of Technology New Delhi India

The research paper introduces an algorithm called SAFARM which performs association rule mining with the help of simulated annealing. It’s a multi-objective problem with vast search space. The suggested approach is independent of the database as it does not require minimum support or minimum confidence specification. In the algorithm, a fitness function is designed to fulfill the required objective and the presentation of rules is proposed with a compact structure. The correctness and efficiency of the algorithm is verified by testing it on synthetic and real databases. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Association rules Data mining Evolutionary algorithm Simulated annealing

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Comparative Analysis for Speech Recognition Using DeepSpeech

Comparative Analysis for Speech Recognition Using DeepSpeech

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Singh, Chanpreet Singh, Aryan Upreti, Himanshu Bhatt, Shobha Sharma, Kirti Netaji Subhas University of Technology Department of Computer Science and Engineering Delhi India Netaji Subhas University of Technology Department of Computer Science Engineering Delhi India

ISBN: (纸本)9798350378092

The presented work aims to implement speech recognition systems for Hindi and English using advanced deep learning. Enhancing ASR technologies is crucial for improving digital communication's accessibility and efficiency across different linguistic contexts, which is pivotal for fostering broader adoption of technology in multilingual environments. The experiments were conducted using speech corpora such as LJSpeech(English Language) and OpenSLR(Hindi Language). The novelty of this research lies in the tailored adaptation of hybrid deep learning architectures, combining the strengths of Convolutional Neural Networks (CNNs) for feature extraction and Recurrent Neural Networks (RNNs) for sequence learning, specifically configured for the acoustic properties of English and Hindi. These architectures are integrated using connectionist temporal classification to enhance transcription precision without the need for linguistic pre-segmentation. The research findings provide evidence that the implemented hybrid deep learning architectures significantly enhance the performance of ASR systems. By specifically addressing the unique challenges posed by Hindi languages, the models demonstrate improved accuracy in transcription, particularly in environments with varied acoustic properties. The system obtained a minimum WER of 21.89. This approach showcases how advanced neural network configurations can adapt to complex linguistic features, thus reducing the Word Error Rate and improving the overall reliability of speech recognition systems in real-world applications. © 2024 IEEE.

关键词： Convolutional neural networks

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ECC based secure privacy-preserving authentication scheme for wireless sensor networks in IoT environment

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International Journal of Information technology (Singapore) 2025年第1期17卷 87-96页

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

The paper suggests a secure privacy-preserving authentication for IoT environments. This study uses Burrows-Abadi-Needham (BAN) logic, Real or Random (ROR) model, and Automated Validation of Internet Security Protocols (AVISPA) to demonstrate that the suggested scheme is robust, provides all needed security features, and overcomes existing scheme drawbacks. The suggested scheme is more effective and secure than competing schemes, based on performance and comparative analyses. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Authentication Elliptic curve cryptography IoT Privacy preservation Security Wireless sensor networks

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Meta-heuristic based feature selection for aberration detection in human activity using smartphone inertial sensors

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International Journal of Information technology (Singapore) 2024年第1期16卷 559-568页

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

Wearable devices are equipped with inertial sensors that can collect motion data and provide objective measures of a person's physical activity. Smartphones are increasingly used to monitor users' activity, enabling accurate qualitative and quantitative measurement. In human activity patterns, aberration refers to any action that deviates from the normal or expected course of action. In the case of physical activity, aberrant activity must be continuously monitored and reported promptly in real-time scenarios. This study employs monitoring real-time physical activity features through the utilization of smartphone inertial sensors, to distinguish between aberrant and non-aberrant activity classes. Data from multiple sensors including accelerometer, gyroscope, magnetometer, and others, are collected from five participants' smartphones and synchronized to monitor activity. To obtain optimal set of statistical features, three meta-heuristic approaches—namely elephant search, wolf search, and cuckoo search—are utilized, and in combination with a correlation attribute, serve as feature filtering methods for feature selection. A new optimal feature set is derived from this process, resulting in a reduction in cardinality by approximately 43% and subsequently reducing the computational power required for analysis. The new feature set is employed to train supervised learning algorithms, which includes four baseline machine learning algorithms. Time to train model has been significantly reduced from 0.34 to 0.16, which is approximately 53% reduction. The results are compared before and after the feature selection process, and a comparative analysis is conducted. Random Forest algorithm proves to be the most effective, achieving an accuracy of 96.57% in predicting activity class. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Aberration detection Feature selection Inertial sensors Metaheuristics Wearable computing

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Artificial Intelligence for Alzheimer's Disease Diagnosis: A Review of Recent Advancements, Obstacles, and Future Prospects

Artificial Intelligence for Alzheimer's Disease Diagnosis: A...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

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

ISBN: (纸本)9798331529833

Alzheimer's disease (AD) is a common chronic ailment that predominantly affects elderly people. AD is a neurological condition that causes a steady deterioration in cognitive function. There is no cure for AD;however, early detection and timely treatment can slow its progression. The development and implementation of artificial intelligence (AI) technology in medicine has greatly improved the accuracy and precision of the detection and diagnosis of AD. Machine learning (ML) and deep learning (DL) models have grown in popularity in recent years, particularly for medical image analysis. The application of ML and DL approaches to diagnose AD has grown significantly in recent years. This study reviews numerous studies from 2020 to 2024 that used ML and DL to detect AD. It gives a complete review of the ML/DL models used in these studies, as well as their limitations and future prospects. © 2025 IEEE.

关键词： Adversarial machine learning

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