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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Santosh, B. Rama Sai Reddy, N. Vinay Kumar Krishna, B. Jathin Vadlamudi, Venkata Naveen Jeyaraj, Jane Rubel Angelina Subhashini, S.J. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu Virudhnagar India Department of IT SRM Madurai College for Engineering and Technology Tamil Nadu India

ISBN: (纸本)9798331300579

Millions of individuals worldwide suffer from knee osteoarthritis (OA), a crippling joint ailment that is marked by joint stiffness, discomfort, and functional impairment. To make a decision severity, physical symptoms, medical history, and further joint screening exams including radiography, MRIs, and CT scans are frequently considered. Unfortunately, the old methods are quite subjective, which makes it difficult to identify early illness development. Worldwide, Osteoarthritis affects close to 240 million individuals. Arthritis's most widespread kind, particularly in older people, is knee OA. Doctors utilize the Kellgren and Lawrence (KL) measure in order to assess the level of severity of knee OA. visually using X-ray or MR imaging. For the intent of detecting and predicting the severity of Knee Osteoarthritis, we are putting forth a model that uses novel Deep Learning models, such as Inception and Exception. By utilizing the KL Grading Scale, we propose ahead a model that can determine the degree of knee osteoarthritis in people. © Grenze Scientific Society, 2024.

关键词： Diseases

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MLP-Based Speech Emotion Recognition for Audio and Visual Features 1

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7th International Conference on Microelectronics and Telecommunication engineering, ICMETE 2023

作者： Kothai, G. Boora, Prabhas Bhanu Muzammil, S. Venkata Subhash, L. Naga Raju, B. KPR Institute of Engineering and Technology Tamil Nadu Uthupalayam Coimbatore India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Krishnankoil India

ISBN: (数字)9789819995622

ISBN: (纸本)9789819995615

Due to its potential applications in domains involving psychology, social smart machines, and human–computer interaction, speech emotion recognition has become an important and growing topic field in the study. That article offers the service comparative analysis of strategies in artificial intelligence classifiers for speech emotion recognition, including MLP, decision tree, SVM, and random forest. Using visual analysis methods like wave plot and spectrogram analysis as well as audio feature extraction, we evaluate these classifiers. According to our observations, MLP achieves better performance than the other classifiers, obtaining an accuracy of 85% when identifying different emotions. Moreover, we illustrate how audio feature extraction and visual analysis help to improve emotion recognition efficiency. Our research has applications for creating speech emotion recognition algorithms that may be applied in real-life scenarios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Decision trees

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An Efficient Machine Learning-Based Power Management System for Smart Grids Using Renewable Energy Resources 2

An Efficient Machine Learning-Based Power Management System ...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Kavya, B.M. Mallikarjunaswamy, S. Sharmila, N. Shilpa, M. Komala, M. Shivaji, R. Balachandra, P. Sheela, S. Pavithra, G.S. Ks Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jagadambha college of Engineering & Technology Dept Electronics and Telecommunication Engineering India Sjb Institute of Technology Dept of Mathematics Bengaluru India Global Academy of Technology Dept of Computer Sceinec and Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

In the dynamic landscape of energy systems, integrating renewable energy resources into smart grids presents substantial challenges, notably due to the inherent variability of wind and solar power. This study unveils an advanced power management system for smart grids that harnesses machine learning algorithms (ApmSGML) to optimize energy distribution and storage, ensuring stability, efficiency, and environmental sustainability. This approach is distinguished from conventional methods such as Fixed Threshold Control and Rule-Based Scheduling, which often struggle to adapt to the unpredictable nature of renewable energy sources. By leveraging predictive models, the proposed system anticipates energy production and consumption patterns, facilitating proactive adjustments to maintain supply and demand equilibrium. Additionally, A comparison of the ApmSGML's performance against traditional power management techniques like Deterministic Load Forecasting and Static Energy Storage Management, underscoring the adaptive and continuous learning advantages offered by machine learning through techniques like deep learning and reinforcement learning. Our findings highlight significant advancements in energy efficiency and cost reduction, demonstrating the transformative potential of integrating machine learning into the energy sector. © 2024 IEEE.

关键词： Internet of things (IoT) Machine learning power management Renewable energy smart grid

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Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's Disease Progression Analysis 4

Brain Imaging and GRU-CNN Hybrid Framework for Alzheimer's D...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Selvam, L. Dhamotharan, R. Krishnan, R. Santhana Francis Raj, J. Relin Sankar, G. Ram Yamini, G. Srm Madurai College for Engineering and Technology Department of Computer Science and Engineering Pottapalayam Sivaganga India Kalasalingam Academy of Research and Education Department of Computer Science and Information Technology Krishnankoil India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Avadi Department of English Chennai India

ISBN: (纸本)9798331523923

Alzheimer's disease detection and classification using deep learning techniques offer significant advancements in early diagnosis and progression analysis of this neurodegenerative disorder. This approach leverages various medical imaging modalities such as MRI, PET, CT, and fMRI, alongside clinical data like cognitive test results and biomarkers, to assess brain changes associated with Alzheimer's. The focus is on classifying the disease into three stages: Mild, Moderate, and Severe, using advanced machine learning models. Deep learning techniques, such as EfficientNet B7 for 2D imaging and 3D CNNs for volumetric data, enable efficient feature extraction from medical scans. Additionally, Recurrent Neural Networks (RNNs) with GRU layers are used to capture temporal patterns from longitudinal data, improving predictive accuracy. Hybrid models, combining CNNs with attention mechanisms, further enhance classification. The model uses a multi-class classification approach with Softmax activation to predict the likelihood of each stage of Alzheimer's disease. Performance evaluation includes key metrics such as accuracy, precision, recall, F1 score, and AUC (Area Under the Curve). AUC can be calculated in two ways: individual AUC scores for each disease stage or a combined AUC for overall classification. This analysis provides a comprehensive understanding of the model's effectiveness in distinguishing between different stages of Alzheimer's. The results can be integrated into clinical workflows to assist healthcare professionals in diagnosing and monitoring Alzheimer's disease progression, offering a potential path toward more efficient and early intervention strategies. © 2025 IEEE.

关键词： Recurrent neural networks

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Fraud detection in Online Payment Transaction using Machine Learning Algorithms 1

Fraud detection in Online Payment Transaction using Machine ...

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1st International Conference on Smart and Sustainable Technologies in Energy and Power Sectors, SSTEPS 2022

作者： Aladakatti, Darshan Gagana, P. Kodipalli, Ashwini Kamal, Shoaib Global Academy of Technology Computer Science and Engineering Bangalore India

ISBN: (纸本)9781665464147

Online payment transaction is a transaction in which payment is made using digitalized currency. Customers all over the world prefer online payments to purchase almost everything from furniture to clothing, from food to medicines, from gadgets to appliances, and *** online transaction has now evolved into many platforms. It is one of the most efficient methods provided by many companies to its customer. It not only helps build the company's revenue but also impacts the growth of the company. Let us not forget that with pros come the cons with the greatest advantage of having tedious transactions done at the fingertips comes the fear of fraudulent activity in which our hard-earned money can get theft within seconds. These activities can be determined by machine learning algorithms by feeding adequate data about these transactions. We have used machine learning algorithms such as SVM (Support Vector Machine), LR (Logistic regression), Naive Bayes, Decision tree, and Random Forrest for the same. It is observed that Random Forest classifier has outperformed comparing to other classifiers with the accuracy of 99.94% © 2022 IEEE.

关键词： Decision trees

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Efficient Brain Tumor Segmentation with Mamba

Efficient Brain Tumor Segmentation with Mamba

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International Conference of computer and Informatics engineering (IC2IE)

作者： Akhilbaran Ghosh Subhadeep Chakraborty Department of Computer Science and Engineering Academy of Technology Adisaptagram West Bengal India Department of Electronics and Communication Engineering Techno International New Town Kolkata West Bengal India

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

This research investigates the application of Structured State Space Sequence Models (SSMs), particularly the Mamba architecture, for brain tumor segmentation in MRI scans. The Mamba model uses selective state spaces to demonstrate efficient and effective segmentation while maintaining linear scalability. Comparative experiments with state-of-the-art models reveal compelling performance in both model size and segmentation accuracy. Our findings indicate the potential of Mamba-based approaches to surpass transformer-based models for this critical medical imaging task, offering a promising alternative in resource-constrained environments. Further refinements and optimizations of Mamba-based models, along with exploration of interpretability and robustness, present avenues for future research and clinical deployment.

关键词： Image segmentation Accuracy Scalability Magnetic resonance imaging Computational modeling Brain modeling Transformers Robustness Optimization Biomedical imaging

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Dual Interspersion and Flexible Deployment for Few-Shot Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年第6期44卷 2732-2744页

作者： Cheng, Ziming Wang, Shidong Long, Yang Zhou, Tao Zhang, Haofeng Shao, Ling Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Newcastle University School of Engineering Newcastle upon TyneNE1 7RU United Kingdom Durham University Department of Computer Science DurhamDH1 3LE United Kingdom University of Chinese Academy of Sciences UCAS-Terminus Ai Lab Beijing100049 China

Acquiring a large volume of annotated medical data is impractical due to time, financial, and legal constraints. Consequently, few-shot medical image segmentation is increasingly emerging as a prominent research direction. Nowadays, Medical scenarios pose two major challenges: (1) intra-class variation caused by diversity among support and query sets;(2) inter-class extreme imbalance resulting from background heterogeneity. However, existing prototypical networks struggle to tackle these obstacles effectively. To this end, we propose a Dual Interspersion and Flexible Deployment (DIFD) model. Drawing inspiration from military interspersion tactics, we design the dual Interspersion module to generate representative basis prototypes from support features. These basis prototypes are then deeply interacted with query features. Furthermore, we introduce a fusion factor to fuse and refine the basis prototypes. Ultimately, we seamlessly integrate and flexibly deploy the basis prototypes to facilitate correct matching between the query features and basis prototypes, thus conducive to improving the segmentation accuracy of the model. Extensive experiments on three publicly available medical image datasets demonstrate that our model significantly outshines other SoTAs (2.78% higher dice score on average across all datasets), achieving a new level of performance. The code is available at: https://***/zmcheng9/DIFD. © 2025 IEEE.

关键词： Prototypes Medical diagnostic imaging Predictive models Training Mathematical models Visualization Semantic segmentation Lesions Law Electronic mail

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Negative Itemset Tree for Discovering Rare Patterns with Periodicity from Static Databases

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engineering Letters 2023年第3期31卷 882-895页

作者： Jyothi Upadhya, K. Bolisetty, Thanishka Rao, B. Dinesh Geetha, M. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Manipal School of Information Sciences Manipal Academy of Higher Education Karnataka Manipal576104 India

Periodic patterns assist in the discovery of crucial information in the fields, including fraud detection, telecommu-nications, retail marketing, research, and medicine. Numerous methods have been developed for the extraction of periodic frequent patterns. The capacity to find uncommon or unexpected combinations that are missed by periodic frequent pattern mining techniques is the main motivation for Periodic Rare Pattern Mining. If the rare patterns are distributed across the transaction dataset, they are periodic and important. A novel PRPNegTidTreeMiner algorithm is proposed to discover rare patterns with periodicity along with support threshold. An efficient tree structure, NegTidTree, is constructed to capture the complete negative representation of the given static database. NegTidTree, serves the dual purpose of finding support count as well as periodicity information. This tree accelerates the extraction of periodic rare patterns and prevents repeated database scanning. PRPNegTidTreeMiner uses two mining techniques to generate periodic rare patterns. The first mining method, NegTidTreeMiner, employs a top-down approach to find rare patterns with periodicity thresholds which avoids traversal of frequent patterns. In the second technique, NegTidTreeMiner-FLP, the mining efficiency is improvised by avoiding the traver-sal of frequent as well as non-existing patterns. Experiments are carried out by varying support and periodicity components for a variety of datasets. The results show that NegTidTreeMiner performs better than NegTidTreeMinerFLP when the dataset is small and generates a huge number of periodic rare patterns. When the size of the database grows, NegTidTreeMinerFLP continuously outperforms NegTidTreeMiner. © 2023, International Association of Engineers. All rights reserved.

关键词： Database systems

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Accident Prediction Using KNN Algorithm 4

Accident Prediction Using KNN Algorithm

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4th International Conference on Emerging Research in Electronics, computer science and technology, ICERECT 2022

作者： Akhiladevi, M. Anitha, K. Amrutha, K. Amrutha, M. Chandanashree, K.R. Global Academy of Technology Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665456357

Today, one of the top priorities of governments istrafficsafety. Given the importance of the subject, identifying the causes of road accidents has become the primary goal in reducing the damage caused by traffic accidents. Machine learning and data mining concepts are usedto recognize the various factors that influence road accidents andtheir severity. The application uses at odd Machine Learning algorithms which includes K-Nearest-Neighbor, Decision tree, Random forest, etc make predictions based on various parameters, within allthese models KNN gives the best accuracy. This informationcan be used to analyze future inputs and improve the system'soutput accuracy. This model can be improved further to send the accident report to the appropriate authorities, such as hospitals, ambulances, and insurance companies, and can thus be very useful in reducing accident fatality rates in the country. © 2022 IEEE.

关键词： Roads and streets

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Research on Spatio-Temporal Data Processing Framework 21

Research on Spatio-Temporal Data Processing Framework

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： He, Liu Ao, Ren Jianping, Li College of Computer Science Chongqing University Chongqing401331 China Chongqing Academy of Educational Sciences Chongqing400015 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

Aiming at the challenges in time series data processing, this paper proposes a new deep learning model, which uses Spiking neurons with time series processing ability to organize, represent and transmit information instead of traditional methods. By combining the time representation characteristics of Spiking neurons with the hierarchical deep connection advantages of complex networks, this study explores a deep learning model combining time and space, which shows significant advantages in the representation and processing ability of time series data. Specifically, we design and implement a model that combines the Spiking mechanism with complex networks. This model can effectively capture the dynamic changes in time series data and improve processing efficiency. The experimental results show that the proposed model has achieved good performance on multiple time series data sets, which verifies its effectiveness and feasibility in time series data representation and processing. This study provides a new method for the field of time series data analysis, which is expected to be widely used in intelligent systems, financial forecasting, bioinformatics and other fields. © 2024 IEEE.

关键词： Spatio-temporal data

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