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Generative Adversarial Networks for Motor Imagery Classification using Wavelet Packet Decomposition and Complex Morlet Transform

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Multimedia Tools and Applications 2025年 1-24页

作者： Shirodkar, Vaishali Edla, Damodar Reddy Kumari, Annu Department of Computer Science and Engineering National Institute of Technology Goa Kottamoll Plateau Goa Cuncolim403703 India Information Technology Department Goa College of Engineering Farmagudi Goa Ponda403401 India

The area of brain-computer interface research is widely spreading as it has a diverse array of potential applications. Motor imagery classification is a boon to several people with motor impairment. Low accuracy and datasets with few trial recordings present challenges for this classification. This research offers a novel approach to these problems in electroencephalography signal classification for efficient control of neuroprosthetic devices. By using wavelet packet decomposition the proposed method divides the EEG signal into eight frequency bands. Each of these bands were then evaluated by a one-dimensional convolution neural network to find the most discriminative band. Only EEG signals filtered with these bands were used to generate time-frequency images called scalograms using complex Morlet transform. These scalograms were then classified using a two-dimensional convolution neural network optimized for spatial-temporal feature extraction and classification. To mitigate data scarcity, generative adversarial networks were used for effective data augmentation. Experimental evaluation on a benchmark dataset demonstrated substantial improvement: the two-dimensional convolution neural network achieved 93.63% accuracy, surpassing the 85% achieved by the one-dimensional convolution neural network baseline. Precision, recall, and F1-score metrics validated robust performance across motor imagery tasks. Comparative analysis with the latest methods underscored the proposed model’s superiority in accuracy and reliability. Using wavelet transformations, convolution neural network architectures, and generative adversarial network-driven data augmentation presented a promising framework for advancing brain-computer interface applications, particularly in enhancing neuroprosthetic device control. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wavelet decomposition

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Street Light Monitoring and Controlling System Using RaspberryPi and Zigbee Protocol 7

Street Light Monitoring and Controlling System Using Raspber...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Gokhale, Pratik Deshpande, Shantanu Page, Saurabh Bagul, Mandar Bundele, Ashwini Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350304268

Street Lighting System is a crucial part of society's amenities and environment. Today's systems consume enormous amounts of electrical energy, for the automation of switches on/off of street lights. This results in a huge loss of energy and money at the same time which needs an efficient system to be developed. We propose a Smart Lighting System which replaces the traditional timer and photocell-based street lighting and outdoor lighting control. It is a complete integrated solution consisting of a Zigbee-based Node integrated with every street light fixture or outdoor lighting fixture, and operating software that uses a Master-Slave concept to control every Node (Slave) from the coordinator module (Master). With energy-saving initiatives, putting the latest technologies in use, this system has the potential to save energy as well as be an effective adaptable way of constructing a lighting system. The system will be deployed on a cloud platform through which the application is remotely accessible. © 2023 IEEE.

关键词： IOT based system Lighting System Raspberry Pi Zigbee

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Securing FANET using federated learning through homomorphic matrix factorization

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International Journal of Information technology (Singapore) 2025年第1期17卷 17-36页

作者： Banerjee, Aiswaryya Mahato, Ganesh Kumar Chakraborty, Swarnendu Kumar Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote 791113 India

As Flying Ad Hoc Networks (FANETs) continue to advance, ensuring robust security, privacy, and data reliability remains a significant challenge. This research presents a novel framework known as HE-FSMF-short for Homomorphic Encrypted Federated Secure Matrix Factorization-specifically designed to tackle these challenges. HE-FSMF integrates matrix factorization with federated learning and homomorphic encryption to enhance both security and efficiency in FANET environments. Matrix factorization, commonly used in recommendation systems, is adapted here to address the unique complexities of FANETs. By leveraging detailed feature extraction through the VGG-16 model, HE-FSMF ensures precise and secure data processing even in dynamic and high-mobility settings. The incorporation of homomorphic encryption protects data throughout cloud-based computations, maintaining privacy and integrity without compromising performance. Additionally, HE-FSMF features mechanisms to verify the accuracy and authenticity of results, which is crucial for establishing trust in distributed systems. This framework not only enhances learning efficiency and improves data transmission rates but also provides strong safeguards for sensitive information. HE-FSMF offers a robust solution for advancing FANET capabilities, making it a valuable tool for secure and efficient communication in the increasingly interconnected and rapidly evolving landscape of networked systems. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： FANET Federated learning Homomorphic encryption UAV

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Leveraging ML Techniques for Image-based Freshness Index Prediction of Fruits and Vegetables 5

Leveraging ML Techniques for Image-based Freshness Index Pre...

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5th International Conference on Emerging Smart Computing and informatics, ESCI 2023

作者： Gokhale, Atharva Chavan, Ameya Sonawane, Sheetal Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9781665475242

Freshness is a prime factor of consideration when purchasing consumables like fruits and vegetables. Studies have proven that computer Vision can be successfully involved in classifying fresh and stale fruits and vegetables and measuring their freshness to some extent. This work attempts to determine and analyze the freshness of fruits and vegetables from their images by proposing a Machine Learning methodology. The entire study was divided into two steps. The first step focused on obtaining classification between images of fresh and stale fruits and vegetables. For this, we trained the ConvNeXt model on an open-source imagery dataset consisting of 12 classes, and it proved efficient by achieving an accuracy of 99.77%. The second step focused on analyzing how fresh a particular fruit or vegetable is from its image. We achieved this by using an open-source dataset of tomato images and extracting features specific to the texture, shape, and colour from these images. Further, we trained classification models on these extracted features and presented the results as quantitative measures with scores of 10 for each of these three factors. Thus, we attempted to achieve an in-depth freshness analysis by grading the images based on these three critical factors while defining freshness. © 2023 IEEE.

关键词： computer vision

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Bi-SMOTE: a novel framework for handling imbalanced datasets using machine learning techniques

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International Journal of Information technology (Singapore) 2025年第1期17卷 431-445页

作者： Tigga, Onima Pal, Jaya Mustafi, Debjani Department of Computer Science & amp Engineering Birla Institute of Technology Jharkhand Mesra Ranchi India

Working with Imbalance data in real-world problems is not so easy due to the different cardinality of classes. Several machine learning Techniques have been used to overcome this kind of problem for 100% original data for classification. In this research work, the proposed Bi-SMOTE approach on the imbalanced Red wine and Glass datasets using machine learning techniques, Random Forest (RF), K Nearest Neighbor (KNN), Logistic Regression (LR), and Decision Tree (DT) have been used. Moreover, preprocessing steps are done by Binarization and standardization. The accuracy and F1-score are calculated of without SMOTE / with SMOTE / proposed Bi-SMOTE approaches with classifiers RF, KNN, LR, and DT for 95% & 100% of original datasets. Here the Bi-SMOTE approach gives better results up to 12%, and up to 22% for accuracy and up to 29% and up to 28% for F1-score for the Red wine and the Glass dataset respectively. Moreover, mean squared error (MSE) is continuously decreased in these machine learning techniques with original data with SMOTE and Bi-SMOTE. Thus, the proposed approach Bi-SMOTE yields the highest values in all performance measures, including accuracy, and F1-score for 95% data than 100% data. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Accuracy Binarization Class Imbalance Machine learning (ML) SMOTE (Synthetic minority over-sampling technique)

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Attention Contours: A Comparative Study on 2D and Virtual Reality Attention Profile Assessment Tests 5

Attention Contours: A Comparative Study on 2D and Virtual Re...

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5th International Conference on Data Intelligence and Cognitive informatics, ICDICI 2024

作者： Rajarajeswari, P. Madhubala, R. Gomathi, V. Ramco Institute of Technology Department of Computer Science and Engineering Rajapalayam India National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (纸本)9798350389609

Virtual Reality (VR) technology in health-care has emerged as a valuable tool for advancing diagnostic techniques and enhancing patient care. This research explores the application of VR in attention profile assessments, with a particular focus on diagnosing Attention Deficit/Hyperactivity Disorder (ADHD). ADHD poses challenges in maintaining attention, and traditional diagnostic approaches often lack real-world relevance. This study examines the efficacy of Attention Contours - a VR-based Continuous Performance Task (CPT) - in comparison to its conventional 2D (two-dimensional) counterpart for assessing attention profiles. Attention Contours VR incorporates varied virtual settings and language-neutral stimuli to improve ecological validity. User trials reveal greater engagement and fewer distractions in the VR environment, highlighting its potential for more accurate attention assessment. Participants showed a strong preference for the VR format, praising its immersive experience and enhanced usability. The findings emphasize the impact of VR in advancing attention assessment tools and suggest its promising role in ADHD assessment. © 2024 IEEE.

关键词： Virtual environments

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Evaluating Deep Learning Algorithms for MRI-Based Brain Tumor Classification

Evaluating Deep Learning Algorithms for MRI-Based Brain Tumo...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Singh, Gurpreet Chhabra, Amit Mittal, Ajay Department of Computer Science and Engineering Chandigarh India University Institute of Engineering and Technology Panjab University Department of Computer Science and Engineering Chandigarh India

ISBN: (纸本)9798350376470

The classification of brain tumors has significant importance in the realm of clinical diagnosis and the implementation of appropriate treatment strategies. The process of diagnosing a brain tumor is time-consuming and strongly dependent on the expertise and experience of the radiologist. The volume of data that necessitates processing has seen a significant surge in parallel with the growth in patient population, rendering previous protocols financially burdensome and inefficient. Numerous researchers have conducted investigations on a range of algorithms aimed at the detection and classification of brain tumors, with a focus on achieving high levels of accuracy and computational efficiency. In recent times, there has been a surge in the utilization of Deep Learning (DL) methodologies to construct automated systems that possess the ability to effectively diagnose or segment brain tumors within a reduced timeframe. Deep learning (DL) facilitates the use of a pre-existing Convolutional Neural Network (CNN) model designed for the analysis of medical pictures, with a special focus on the classification of brain tumors. The rapid advancement of Deep Learning techniques in the context of computer vision tasks has been made possible by the availability of extensive training datasets and advancements in model architecture, leading to enhanced approximations within a supervised learning framework. The growth has mostly been motivated by the need to implement these ideas. The use of deep learning methods has shown potential in enhancing the accuracy of brain tumor identification and classification via the utilization of magnetic resonance imaging (MRI). According to the findings, CNN is a superior technique that greatly boosts the accuracy by 93.4% and it is maximum as compared to other methods. © 2024 IEEE.

关键词： Convolutional neural networks

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HyPar-FCA+: an improved workload-aware elastic framework for FCA

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Journal of Supercomputing 2023年第11期79卷 11767-11796页

作者： Packiaraj, Muneeswaran Kailasam, Sriram Indian Institute of Technology Mandi Mandi India Department of Computer Science and Engineering National Institute of Technology Warangal Warangal India

Formal concept analysis (FCA) algorithms are computationally expensive. Several parallel and distributed FCA algorithms are proposed to reduce the execution time by taking advantage of the parallelism available in the cloud environment. These approaches can be broadly classified into replication-based and partitioning-based strategies. Replication-based strategies require the entire input data to be in memory throughout execution, and therefore, they suffer from memory bottlenecks while dealing with large datasets. Horizontal partitioning-based approaches overcome the memory bottlenecks but incur enormous communication overhead during concept discovery as they require all-to-all worker communication per concept. Thus, the state-of-the-art frameworks implementing the above strategies do not scale to large datasets in cloud environments running commodity hardware. In this paper, we propose HyPar-FCA+, an improved workload-aware elastic framework for FCA, that overcomes the above scalability issues and also improves resource utilization/cost in a cloud environment. Its salient features are a novel vector bin packing-based algorithm for partitioning the input context, a new workload estimator, and elastic provisioning of cloud resources using a feedback-based predictor. Compared with state-of-the-art distributed FCA frameworks, HyPar-FCA+ is 3–11% and 15–36% faster for real-world datasets (Susy, Webdocs) and synthetic datasets, respectively. In terms of execution cost in the cloud, HyPar-FCA+ takes 27–38% and ∼ 85% less cost for real-world datasets and synthetic datasets, respectively. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Formal concept analysis

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Effective & Smart Urban Mobility System at Low Cost 2

Effective & Smart Urban Mobility System at Low Cost

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Jyothula, Hari Rao, Y Laxmana Prasad, K. Vara Usha, Chiriki Manikyamba, I Lakshmi Aditya University Department of Computer Science and Engineering Surampalem India Vignan's Institute of Engineering for Women Department of It Visakhapatnam India Avanthi Institute of Engineering & Technology I Department of Computer Science and Engineering India Dadi Institute Of Engineering And Technology Department of Computer Science and Engineering India University College of Engineering Science & Technology Hyderabad Jawaharlal Nehru Technological University Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350364699

There is no established metric for measuring urban smartness at this time. Various writers use various metrics to assess smart city transportation systems. The assessment indicators for the transport system that were offered in the scholarly works were reviewed, and the most relevant and impactful indicators were chosen. A hierarchical assessment model for smart city transport systems is developed in this paper. There are five categories, or "factors,"that the indications fall into. Several indicators are assigned to each of the categories. This study used the hybrid MCDM (multi-criteria decision-making) method to evaluate urban transport systems and identify which of the given characteristics were most important. Top Priority Sorting based on Ideal Solution Similarity (TOPSIS), COPRAS, and Simple Additive Weighting (SAW) were the multi-criteria assessment methods in use. The importance of the components and indicators was determined using an analytical hierarchy process (AHP), rankings (prioritization), and direct evaluation (experts evaluate each criterion as a percentage;total assessments of all criteria equal 100%). There were two phases to the evaluation and comparison of mobility systems: first, smart mobility systems were evaluated comprehensively according to the integrated importance of the indicators;and second, multi-criteria evaluations were conducted according to the indicators of every factor separately. Based on the amount of intelligence, a top city is determined and rated. Building a hierarchical model for evaluating smart transportation systems is the focus of this paper. © 2024 IEEE.

关键词： Evaluation smart transportation MCDM Smart City Smart Mobility System Urban smartness

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NLP based Text Summarization of Fintech RFPs 2

NLP based Text Summarization of Fintech RFPs

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Patil, Krutika Badamikar, Medha Sonawane, Sheetal Pune Institute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9781665491990

In today's day and age, where huge quantities of textual data are generated every second, it has become difficult to keep ourselves abreast with new information. Documents in the financial sector tell a quantitative story. The qualitative or language content that accompanies financial statements is an essential component of the information set used by financial market participants for monitoring and stewardship. This necessitates the development of efficient technological methods for leveraging the existence of these massive amounts of textual data. Reading financial documents such as annual reports is extremely time-consuming and thus corporations have to allocate precious human capital to understand, analyze and derive insight from these reports. As a result, automatic summarization techniques can make this task simpler by enabling access to a smaller but informative portion of a given document. In this work, a system utilizing NLP techniques for summarizing financial documents based on queries provided by the user, is presented. This system overcomes the challenges posed by existing section-based summarization models. As machine learning methods have proven to be effective on downstream tasks such as text summarization, the intent of this work is to take advantage of these methods for effective extractive summarization and generate human-like summaries. The proposed model categorizes phrases according to their relevance by leveraging the strength of unsupervised clustering approaches, and it gets a ROUGE-1 score of 46%. © 2023 IEEE.

关键词： Natural language processing systems

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