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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Venkatesh, S. Jeevitha, D. Gnanaselvi, J. Anitha Srm Institute of Science And Technology Department of Data Science And Business Systems School of Computing Chennai Kattankulathur India Jeppiaar Engineering College Department of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350365092

The human brain has a simple time analyzing and processing images. The brain is able to rapidly deconstruct and distinguish an image's various components when the eye perceives it. With the Convolutional Neural Network (CNN) as its foundation, this research suggests deep learning conceptual models. When the algorithms are compared, it becomes clear that CNN-based classification of handwritten alphabets performs better than other algorithms in terms of accuracy. The Manual Net, Alex Net, and LeNet Architectures are among the CNN algorithms employed in this research. The convulational layer, max pooling, flattening, feature assortment, rectifier lined unit, and completely linked softmaxx layers are each components of the aforementioned designs. The proposed network is tested using an image dataset comprising 530 training photos and 2756 testing images. The top precision and cost-efficient model will be used in the Django context to build a handler line for supplying the appeal to be recognized and obtaining the productivity outcome of recognized appeal. © 2024 IEEE.

关键词： Convolutional neural networks

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Performance of pre-trained deep learning models for land use land cover classification using remote sensing imaging datasets

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Environmental Earth sciences 2025年第11期84卷 1-36页

作者： Haider, Irfan Khan, Muhammad Attique Masood, Saleha Algamdi, Shabbab Ali Alasiry, Areej Marzougui, Mehrez Nam, Yunyoung Department of Computer Science HITEC University Taxila Pakistan Department of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia IRC for Finance and Digital Economy King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Software Engineering College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al Kharj Saudi Arabia College of Computer Science King Khalid University Abha61413 Saudi Arabia Department of Computer Science and Engineering Soonchunhyang University Asan31538 Korea Republic of

This study presents a comparative analysis of ten pre-trained convolutional neural network (CNN) models, evaluated across three remote sensing datasets: EuroSat, NWPU, and Earth Hazards (Land Sliding). We investigate the interplay between model architecture and classifier selection by incorporating five different neural network (NN) classifiers, emphasizing their impact on predictive accuracy and computational efficiency. Due to its densely connected architecture, DenseNet201 achieved the highest accuracy—97% on EuroSat, 99.40% on NWPU, and 97.80% on Earth Hazards. In contrast, MobileNetV2, while slightly less accurate, demonstrated superior computational efficiency, recording the shortest prediction times of 39.943 s on EuroSat, 27.482 s on NWPU, and 2.8986 s on Earth Hazards. Additionally, classifier choice significantly influenced performance, with the Wide NN classifier excelling in diverse datasets and the Medium NN classifier optimizing speed. Our findings underscore the importance of balancing accuracy and efficiency in selecting CNN models for remote sensing applications, suggesting future research should explore ensembling techniques and lightweight models to enhance performance. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Software design

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Ensemble Based Precautionary Detection of Parkinson's Disease from Voice Signal 1

Ensemble Based Precautionary Detection of Parkinson's Diseas...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Bhanusree, Yalamanchili Adhvy, Vodnala Sharan Affan, Mohammed Shaji Praneetha, Kanvapuri Sai Sahithi, Katikireddy Sai Computer Science - Data Science VNR Vignana Jyothi Institute of Engineering & Technology Hyderabad India

ISBN: (纸本)9798331518943

Parkinson's Disease is a neurological progressive disorder that affects the nervous system of a human being, which effects the parts of the body. Tremors, imbalance, slowed movement, stiffness, difficulty in walking and talking etc. are observed. The reasons being, brain disorder, injuries, toxins certain drugs etc. The disease has no cure however treatment can be offered for relief from symptoms. However early detection can cure the disease in the first stages and extend the life span of the patient. This paper proposes a Ensembled learning model for early detection of Parkinsons disease through voice features. Random forest ensemble models have outperformed the Machine learning and Neural Network models in detecting disease from voice. An accuracy of 94.8% is achieved with this model with proper preprocessing techniques and training. As a future work the proposed model's software prototype can be released for mobile application for cost effective early detection of Parkinsons Disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Attention-Based Speech Enhancement Using Human Quality Perception Modeling

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 250-260页

作者： Nayem, Khandokar Md. Williamson, Donald S. Indiana University Department of Computer Science BloomingtonIN47408 United States Ohio State University Department of Computer Science and Engineering ColumbusOH43210 United States

Perceptually-inspired objective functions such as the perceptual evaluation of speech quality (PESQ), signal-to-distortion ratio (SDR), and short-time objective intelligibility (STOI), have recently been used to optimize performance of deep-learning-based speech enhancement algorithms. These objective functions, however, do not always strongly correlate with a listener's assessment of perceptual quality, so optimizing with these measures often results in poorer performance in real-world scenarios. In this work, we propose an attention-based enhancement approach that uses learned speech embedding vectors from a mean-opinion score (MOS) prediction model and a speech enhancement module to jointly enhance noisy speech. The MOS prediction model estimates the perceptual MOS of speech quality, as assessed by human listeners, directly from the audio signal. The enhancement module also employs a quantized language model that enforces spectral constraints for better speech realism and performance. We train the model using real-world noisy speech data that has been captured in everyday environments and test it using unseen corpora. The results show that our proposed approach significantly outperforms other approaches that are optimized with objective measures, where the predicted quality scores strongly correlate with human judgments. © 2014 IEEE.

关键词： Speech enhancement

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Aggregated Relative Similarity (ARS): a novel similarity measure for improved personalised learning recommendation using hybrid filtering approach

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Multimedia Tools and Applications 2025年第18期84卷 18733-18780页

作者： Pal, Saurabh Dutta Pramanik, Pijush Kanti Choudhury, Prasenjit Department of Computer Science and Engineering National Institute of Technology Durgapur West Bengal Durgapur India School of Computer Applications and Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science and Engineering School of Engineering Brainware University Kolkata India

To improve the effectiveness of online learning, the learning materials recommendation is required to be personalised to the learner material recommendations must be personalized to learners. The existing approaches are ineffective in recommending learning materials according to the learner’s learning situation, the learner’s internal learning characteristics, and fitting to the learner’s preferences and learning suitability. In this work, we propose a hybrid recommendation approach cthat combines the advantages of knowledge-based and collaborative filtering approaches. The knowledge-based filtering approach enables us to find the most suitable learning materials that fit the learner’s situation and learning state by appropriately mapping the learners’ contexts with the metadata of the learning materials. We use collaborative filtering to refine learning material selection by considering learner-learner similarity. We propose a novel similarity measure, aggregated relative similarity (ARS), to determine similar learners based on implicit learner characteristics such as education, knowledge, cognitive ability, and learning style. The experimental results show that the proposed ARS algorithm outperforms popular similarity measures such as Jaccard similarity, Sorensen-Dice coefficient, and overlap coefficient. The overall recommendation performance of the proposed hybrid approach attained mean absolute error (MAE) and root mean square error (RMSE) of 0.681 and 0.9198, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Collaborative filtering

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Agreements of Nonlinear Opinion Dynamics in Switching Social Networks

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IEEE Transactions on Automatic Control 2024年第6期69卷 4174-4181页

作者： Lee, Ti-Chung Su, Youfeng National Sun Yat-sen University Department of Electrical Engineering Kaohsiung80424 Taiwan Fuzhou University College of Computer and Data Science Fuzhou350116 China

This article formulates and analyzes agreements of the nonlinear opinion dynamics in social networks according to switching interactions, where the agents' susceptibilities depend on current states. These switching interactions are formulated by switching directed graphs with some mild joint connectivity, and depicted by nonlinear switching model. An extended LaSalle invariance principle is established for analyzing the agreements of this switching model as well as three specialized scenarios, where the limiting equations formed by the weak∗ convergence are used to study the steady-state behavior so that the stability analysis is substantially simplified in the sense that the 'max - min' Lyapunov function may be assumed to be constant along any bounded forward complete solutions of limiting equations. Examples and simulations are used to verify the obtained results. © 1963-2012 IEEE.

关键词： Social networking (online)

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Assessing U-Net Architectures for Effective Brain Tumor Segmentation in MRI

Assessing U-Net Architectures for Effective Brain Tumor Segm...

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

作者： Verma, Akash Yadav, Arun Kumar Department of Computer Science and Engineering India

ISBN: (纸本)9798331529833

Accurately segmenting brain tumors using MRI scans is essential for diagnosing, treatment planning and monitoring treatment. This research compares several U-Net based architectures, including U-Net DIU-Net, DFP-ResUNet, BU-Net, and SEResU-Net, which are implemented and evaluated using BraTS 2018 dataset. Each model integrates unique enhancements to address challenges in brain tumor segmentation, like vanishing gradients, poor feature extraction, & class imbalance and trained using BraTS 2018 dataset, and their performance is evaluated using Accuracy, Dice score, and IoU score. The comparative analysis reveals that while each model has specific advantages, the SEResU-Net and BU-Net architectures attain the highest performance metrics, especially in segmenting TC and ET regions. This evaluation of U-Net variants segment brain tumor highlights their strengths and limitations, offering key insights for advancing brain imaging research and clinical. © 2025 IEEE.

关键词： Deep learning

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Application of adaptive guard channel reservation under hard handoff constraint in wireless cellular network

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International Journal of Wireless and Mobile Computing 2025年第2期28卷 170-185页

作者： Sahu, Promod Kumar Pati, Hemanta Kumar Pradha, Sateesh Kumar Department of Computer Science and Application Utkal University Vani Vihar Odisha Bhubaneswar India Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar India

Voice is the king of communication in wireless cellular network (WCN). Again, WCNs provide two types of calls, i.e., new call (NC) and handoff call (HC). Generally, HCs have higher priority than NCs because call dropping gives negative impact to users as compared to call blocking. So, to handle this problem some channels are reserved for HCs. If more channels are reserved for HCs, then handoff call dropping probability (HCDP) decreases at the same time as new call blocking probability (NCBP) increases;if fewer channels are reserved for HCs, HCDP increases and NCBP decreases. If channels are reserved for HCs by considering target HCDP then HCDP will be below the target and NCBP will be minimum for that target. So, keeping this in mind in this paper, we propose a mathematical model that estimates optimal number of guard channels (GCs) for HCs by considering target HCDP and keeping NCBP minimum. The mathematical model proposed assume static traffic parameters. However, in reality, the user traffic is dynamic and usually inherited through its testbed implementation. To obtain optimal performance we propose an adaptive GC reservation scheme by considering hard handoff constraint while implementing this model through testbed. Finally, we have verified testbed results with those of the model-based approach applying the parameters of different Global System for Mobile Communication (GSM) standards to evaluate its applicability to real systems. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Testbeds

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Optimizing Clustering in Wireless Sensor Networks: A Synergistic Approach Using Reinforcement Learning (RL) and Particle Swarm Optimization (PSO)

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SN computer science 2024年第6期5卷 718页

作者： Choudhary, Arpita Barwar, N.C. Computer Science and Engineering Department MBM University

Significant research efforts are currently devoted to wireless sensor networks due to its broad range of applications. WSNs face various constraints, encompassing challenges related to communication, clustering management and the finite battery life of nodes. Thus, Energy conservation in such networks is indispensable. Given a constant energy consumption rate during information sensing and reception, the highest energy consumption among sensor nodes occurs during data transmission. One of promising solution to reduce energy consumption is organizing WSN in clusters. Clustering in Wireless Sensor Networks (WSN) involves grouping sensor nodes into clusters to facilitate efficient data aggregation, communication, and management within the network. This organizational structure helps optimize energy consumption, enhance scalability, and prolong the overall lifespan of the WSN. However determining the optimal criteria for selecting cluster heads is challenging, as it involves balancing energy efficiency, network connectivity, and load distribution. In this paper, a dual-phase approach is proposed, firstly Reinforcement learning (RL) approach has been applied to clustering in WSNs which enables nodes to autonomously adapt their clustering strategies, leading to more efficient and adaptive network configurations. Further Particle Swarm Optimization (PSO) can be utilized for cluster head selection in Wireless Sensor Networks (WSNs) to optimize the formation of clusters. The consideration of both local and global perspectives in the proposed approach results in a more balanced and efficient clustering solution. The outcomes of our experiments demonstrate the enhanced performance of the integrated approach as compared to traditional clustering algorithms. Results show considerable improvement in terms of reduced energy consumption, accuracy and efficiency in fault detection specifically tailored for Wireless Sensor Networks (WSNs). In addition the proposed algorithm show enha

关键词： Clustering Energy-saving mechanisms Optimization techniques Particle Swarm Optimization Reinforcement learning Wireless Sensor Networks

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Language Translator Using Python 1

Language Translator Using Python

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Raj, Mayank Devang, Ashish Chanchal, Satya Saini, Kavita Galgotias University School of Computer Science and Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798350356816

The most well-known type of correspondence between people is Discourse and correspondence assumes a vital part for conveying data. The truth of the matter is that each specific person on the planet won't discuss the same language, numerous people will be communicating in various dialects. Summing up the required content is particularly vital to conveying the right message. On the off chance that the data required isn't conveyed appropriately then numerous entanglements might happen. If either side of the imparting people is uninformed about the language, then the pattern of discourse will be deficient. Thus, we want a framework that will settle our language obstruction issue. So, we concocted a framework for example "Language Convertor utilizing Python"which will change over discourse of one language into another dialect utilizing Python. project intends to foster a flexible and easy-to-use instrument that can consistently interpret text between different dialects. This undertaking uses the force of Python, alongside famous libraries like Google Decipher Programming interface or DeepL API, to make a proficient language change device. © 2024 IEEE.

关键词： Python

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