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Enhancing patient-independent detection of freezing of gait in Parkinson’s disease with deep adversarial network

Neural Computing and Applications 2025年 1-21页

作者： Fahad, Md Shah Ranjan, Ashish Kumar, Gautam Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India Department of Computer Science and Engineering C. V. Raman Global University Odisha Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi India

Freezing of gait (FoG) refers to sudden, relatively brief episodes of gait arrest in Parkinson’s disease, known to manifest in the advanced stages of the condition. Events of freezing are associated with tumbles, traumas, and psychological repercussions, significantly impacting the patient’s quality of life. The use of accelerometer data derived from sensors put on a patient’s body to detect FoG has previously been proposed using convolutional neural network (CNN)-based deep learning algorithms. Here, we combine a CNN + Long short-term Memory (LSTM)-attention model to detect FoG episodes—a first for the detection of FoG using the accelerometer data. CNN facilitates automatic feature extraction from the accelerometer data itself. The output from the CNN is fed to the LSTM network, which is known for capturing sequential information. Further, the attention mechanism introduces relative focus on individual sub-sequences during the training of the LSTM network. The proposed model is made patient independent using adversarial training. The proposed model achieved improvements of +7.60% (without adversarial training) and + 8.36% (with adversarial training) in the sensitivity values over state of the art. This is obtained with little or no compromise in specificity values. The corresponding improvements in accuracy values are +3.81 and +2.23%, respectively. These findings imply that the proposed method can detect FoG gaits satisfactorily and can be effective in achieving accurate monitoring and gait assistance for Parkinson’s disease patients during daily life and rehabilitation therapy. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Accelerometers

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An efficient signed SSL/TLS-based data security in the cloud using LTT-DDBM and TECC

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International Journal of Information technology (Singapore) 2024年 1-16页

作者： Tiwari, Chandra Sekhar Jha, Vijay Kumar Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi India

Cloud data Security (CDS) is a set of strategies for securing data from security threats. However, the previous works ignored the prevention of multiple cloud security attacks. To tackle this problem, an efficient security framework using Signed Secure Socket Layer/Transport Layer Security (SSL/TLS) with Log Triangular Transfer function-based Deep Drop Boltzmann Machines (LTT-DDBM) and Rivest-Shamir-Adleman Session Key-centric Twisted Edwards Curve Cryptography (RSASK-TECC) techniques is proposed in this paper. The process begins with the web owner who requests an SSL/TLS certificate from the Certificate Authority Centre (CAC) for establishing a website. After meeting CAC requirements, the web owner uploads files to the server. The data Owner (DO) logs in and the signed SSL/TLS certificate is sent to the CAC for verification using LTT-DDBM. After the successful verification, a public key and session key are generated to encrypt the data. The data is further encrypted within 1124 ms using Advanced Encryption Standard Session key-based Twisted Edwards Curve Cryptography (AESS-TECC) for enhanced security. Before downloading, the data user checks the hash codes for integrity. If the codes match, then the access is granted;or else, it is denied. Hence, the proposed work outperformed the traditional methodologies. © Bharati Vidyapeeth's institute of Computer Applications and Management 2024.

关键词： Advanced encryption standard session key based twisted Edwards curve cryptography (AESS-TECC) Cloud data security Digital signature algorithm (DSA) Hashing algorithm Log triangular transfer function based deep drop Boltzmann machines (LTT-DDBM) Secure socket layer/transport layer security (SSL/TLS)

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Impact of Variable Sample Size on the Efficiency of Support Vector Machines in Cardiovascular Disease Detection 2

Impact of Variable Sample Size on the Efficiency of Support ...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Sarraju, Vijayalakshmi Pal, Jaya Kamilya, Supreeti Computer Science and Engineering Department Birla Institute of Technology Lalpur Ranchi Mesra India Computer Science and Engineering Department Birla Institute of Technology Ranchi Mesra India

ISBN: (纸本)9798350383867

Exploring the relationship between sample size and the performance of machine learning models in diagnosing cardiovascular diseases (CVD) aids in understanding the optimal data volume required to achieve reliable and accurate diagnostic predictions. This research focuses on examining how the size of the sample influences the efficacy of a particular machine learning algorithm, Support Vector Machine (SVM) in detecting cardiovascular diseases. The efficacy of the SVM models was assessed by employing datasets with different sample sizes. These include one original dataset and two statistically representative samples with sample sizes of 170 and 208 respectively. We evaluate multiple metrics, including F1-score, accuracy, specificity, sensitivity, precision, AUC and ROC curve. Based on the findings, it is evident that a larger sample size generally leads to improved precision and specificity. However, it is important to note that a decrease in sensitivity often accompanies this improvement. In addition, the study highlights the importance of using multiple performance metrics to comprehensively assess the efficacy of SVM models. ©2024 IEEE.

关键词： Support vector machines

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Accurate Brain Tumor Segmentation and Detection using Multi-Task Learning with GlobalNet and FusionNet 12

Accurate Brain Tumor Segmentation and Detection using Multi-...

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12th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2023

作者： Shreyanth, S. Niveditha, S. Kathiroli, V. Birla Institute of Technology and Science Data Science and Engineering Pilani333031 India Rajalakshmi Engineering College Department of Biotechnology Chennai602105 India Birla Institute of Technology and Science Automotive Engineering Pilani333031 India

ISBN: (纸本)9781665462617

Accurate and quick brain tumor diagnosis and treatment are critical for the successful management of these disorders, which can have a substantial influence on an individual's quality of life and even be deadly. This work describes a novel technique for brain tumor segmentation and classification that employs three deep learning architectures: GlobalNet, Multi-task Learning, and FusionNet. GlobalNet is a convolutional neural network that incorporates global context information into the model via a global pooling layer, whereas Multi-task Learning allows the model to do numerous tasks concurrently, enhancing each task's performance by using shared knowledge among them. FusionNet is a deep fully residual convolutional neural network that combines information extracted from multiple CNNs trained on various medical imaging modalities to improve the accuracy and resilience of brain tumor segmentation. The proposed method is shown to be robust and accurate in segmenting and categorizing brain tumors, providing an effective solution with better accuracy in predicting the brain tumors and segmenting the affected part. © 2023 IEEE.

关键词： Convolutional neural networks

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Efficiency-optimized 6G:A virtual network resource orchestration strategy by enhanced particle swarm optimization

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Digital Communications and Networks 2024年第5期10卷 1221-1233页

作者： Sai Zou Junrui Wu Haisheng Yu Wenyong Wang Lisheng Huang Wei Ni Yan Liu College of Big Data and Information Engineering Guizhou UniversityGuiyangChina Prospective Technology Lab of the China Future Internet Engineering Center BeijingChina College of Computer Science and Engineering University of Electronic Science and TechnologyChengduChina International Institute for Next Generation Internet Macao University of Science and TechnologyMacaoChina Data61 Business Unit CSIROAustralia

The future Sixth-Generation (6G) wireless systems are expected to encounter emerging services with diverserequirements. In this paper, 6G network resource orchestration is optimized to support customized networkslicing of services, and place network functions generated by heterogeneous devices into available *** is a combinatorial optimization problem that is solved by developing a Particle Swarm Optimization (PSO)based scheduling strategy with enhanced inertia weight, particle variation, and nonlinear learning factor, therebybalancing the local and global solutions and improving the convergence speed to globally near-optimal *** show that the method improves the convergence speed and the utilization of network resourcescompared with other variants of PSO.

关键词： Virtualization Network function orchestration Network resource virtualized orchestration (NRVO) Particle swarm optimization(PSO)

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Thermal conductivity enhancement of magnetic nanofluids for energy applications

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Materials Today: Proceedings 2023年 72卷 67-73页

作者： Manoj Kumar, Puppala Parameshwaran, R. Sreedhar, I. Department of Mechanical Engineering Birla Institute of Technology and Science-Pilani Hyderabad Campus Hyderabad 500 078 India Department of Chemical Engineering Birla Institute of Technology and Science-Pilani Hyderabad Campus Hyderabad 500 078 India

Nanofluids are a class of fascinating energy transfer fluids that are gaining impetus in many heat transfer applications due to their tremendous potential in augmenting the thermal properties of the base fluids such as water, ethylene glycol, engine oil and so on. From this perspective, this study was aimed at investigating the enhancement of thermal conductivity of distilled water (base fluid) using magnetic nanoparticles. The magnetic nanoparticles were experimented for understanding their surface morphology, crystal structure, chemical stability, thermal conductivity and viscosity using the respective characterization techniques. The scanning electron microscopy results infer that the average size of the agglomerated spherically shaped magnetic nanoparticles was 600.8 nm, however the size of individual particles was observed to be well within 100 nm. The X-ray diffraction pattern showed prominent intense peaks at 29.87°, 35.61° and 63° which have justified the crystalline nature of the nanoparticles. The as-prepared magnetic nanofluids (MNF) when tested for its surface structure has revealed good chemical stability between the base fluid and the magnetic nanoparticles. It is noteworthy that, the thermal conductivity of the MNF was ranging from 0.7666 W/m K to 0.9666 W/m K for the volume fraction of nanoparticles ranging from 0.01 % to 0.1 % and temperature ranging from 35° C to 55 °C. The significance of this work has been justified in terms of achieving enhanced thermal conductivity ranging from 21.3 % to 53.1 % for such low volume fractions of the nanoparticles. Furthermore, the MNF exhibited only marginal variations in viscosity for the specified volume fractions of the nanoparticles. In summary, the as-prepared MNF with these enhanced attributes can be considered to be viable and beneficial for the energy applications. © 2022

关键词： Volume fraction

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Ni-Co doped TiO2 catalyst for efficient photocatalytic degradation of Malachite Green under UV and direct sunlight

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Environmental science and Pollution Research 2025年 1-16页

作者： Mhalshekar, Krushika Pawar, Vivek Gaydhane, Mrunalini Sontakke, Sharad Materials and Process Development Laboratory Department of Chemical Engineering Birla Institute of Technology and Science K. K. Birla Goa Campus Goa Pilani403726 India Department of Chemical Engineering Thapar Institute of Engineering and Technology Punjab Patiala147004 India

In the present study, combustion-synthesized TiO2 nanoparticles were wet impregnated with Ni, Co, and Ni-Co, respectively. The photocatalytic performance of synthesized catalysts was evaluated against Malachite Green dye. The synthesized materials were characterized for crystallite size, surface morphology, elemental composition, and band gap using X-ray diffraction, scanning electron microscopy, energy dispersive spectroscopy, and ultra-violet diffused reflectance spectroscopy, respectively. The optimum parameters for maximum degradation were found by examining the effects of catalyst loading, initial dye concentration, and light intensity. A comparative analysis of Ni-doped, Co-doped, and Ni-Co-doped TiO2 photocatalysts was conducted. The results indicate superior photocatalytic activity of Ni-Co doped TiO2 among the catalysts investigated under UV light. The degradation kinetics was studied, and the underlying degradation mechanism was proposed with the help of LC–MS analysis. Furthermore, a comparative study on the degradation under solar radiation using Ni-Co/TiO2 was conducted. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Energy dispersive spectroscopy

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Autonomous Recommendation of Fault Detection Algorithms for Spacecraft

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 273-275页

作者： Wenbo Li Baoling Ning the National Key Laboratory of Science and Technology on Space Intelligent Control Beijing Institute of Control EngineeringBeijing 100094China the School of Data Science and Technology Heilongjiang UniversityHarbin 150001China

Dear Editor, This letter deals with the problem of algorithm recommendation for online fault detection of spacecraft. By transforming the time series data into distributions and introducing a distribution-aware measure, a principal method is designed for quantifying the detectabilities of fault detection algorithms over special datasets.

关键词： fault introducing letter

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A Long Short-Term Memory Network-Based Approach for Predicting the Trends in the S&P 500 Index

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 19-26页

作者： Siddesh, G.M. Sekhar, S. R. Mani Srinivasa, K.G. Department of Artificial Intelligence & Data Science M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Data Science and Artificial Intelligence Programme International Institute of Information Technology Naya Raipur India

The price level of a considered security in the stock market is largely determined by perceived consumer demand. Variations in price level in the stock market are essentially a manifestation of public psychology aiming to either cut losses or maximize profits. The assumption that market movements are controlled by a guiding trend is proved by establishing that the time-series variations of daily S&P 500 indices are a non-stationary process using the Dickey–Fuller test. This paper presents a long short-term memory (LSTM) network adaptation of an autoregressive predictive model for S&P 500 indices. Later, an LSTM network-based model is devised to learn the underlying patterns that are responsible for driving the trends in market dynamics. The proposed model accepts as input at each time-step a vector of prior S&P indices over the past two weeks and estimates the current market index. The network weights assume the correct settings by training the model using daily S&P indices over the past ten years. The proposed model decisively captures subtleties in market trends, which are in turn verified using the Granger causality approach. The result demonstrates that the proposed model achieved an accuracy of 82.6%, whereas random forest results in 79.12%. The results obtained are truly the latest in this line of predictive models and reaffirm that LSTM networks are powerful learning models capable of deciphering intricate underlying patterns. © 2023, The Institution of Engineers (India).

关键词： Long short-term memory

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Identification of Human Activity from Video Streaming Smartphone data Using Intensified VGG16

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International Journal of engineering, Transactions B: Applications 2025年第6期38卷 1340-1352页

作者： Yadav, R.K. Daniel, A. Semwal, V.B. Department of Computer Science and Engineering Amity University Gwalior India Data Science and Engineering Manipal University Jaipur India Department of Computer Science and Engineering Maulana Azad National Institute Technology Bhopal India

Human activity recognition (HAR) techniques pick out and interpret human behaviors and actions by analyzing data gathered from various sensor devices. HAR aims to recognize and automatically categorize human activities using patterns and attributes taken from sensor data. HAR is complex in implementing the algorithm for a self-recorded dataset, including challenges such as age variation, wearing different clothes, environment and surface, the direction of the smartphone camera, and many more. The paper aims to propose a VGG16 deep learning framework including an activation function and different optimizers for classifying human activity from the real-time captured dataset;further, we compare the evaluated results with existing results. The proposed methods achieved 99.88% accuracy with excellent precision, recall, and F_measure values. Comparing the evaluated result with existing outcomes over the WISDM and UCI-HAR datasets. The new things in the article are a self-captured dataset of various aged male, female, and healthy volunteers to perform seven activities. Furthermore, this research uses Tensor Processing Units (TPU) available on Kaggle to improve classification accuracy while reducing error rates and speeding up execution. ©2025 The author(s).

关键词： Smartphones

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