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A hybrid approach for Alzheimer’s disease diagnosis: image segmentation and deep learning classification

Neural Computing and Applications 2025年 1-19页

作者： Bhimavarapu, Usharani Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Alzheimer’s disease is a severe neurodegenerative disease that leads to cognitive decline and memory loss, and hence, the early diagnosis is essential for proper management and care. This study investigates the usage of the improved fuzzy C-means (FCM) algorithm for segmentation of MRI scan and five deep learning models, namely Densenet121, Inception-v3, VGG-19, Xception, and Resnet-50, for image classification. Enhanced fuzzy C-means algorithm helps segment MRI data, while the selected deep learning architectures classify these images for accurate classification. Rigorous processes, including five-fold cross-validation, early stopping, and optimization techniques, were employed to ensure model performance. The results indicated that combining the enhanced fuzzy C-means algorithm with deep learning architectures significantly improved MRI classification accuracy, where Densenet121 and Resnet-50 exhibited the best results. This approach holds promise for enhancing the diagnosis and treatment of Alzheimer’s disease because it increases detection accuracy and reliability, allowing for better patient care. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Neurodegenerative diseases

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Investigating the role of electroencephalogram based rhythmic bands: applications in imagined speech classification and epileptic seizure severity detection

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International Journal of Speech Technology 2024年第4期27卷 1135-1147页

作者： Banerjee, Oindrila Govind, D. Speech Research Group Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Greenfields AP Vaddeswaram522302 India

The objective of this paper is to explore the significance of rhythmic bands for imagined speech classification and the severity detection of epileptic seizures from electroencephalogram (EEG) signals. Non-invasive decoding of imagined speech (words or sentences imagined in the brain) finds important applications in patients with lock-in syndrome and other neurological disorders. In the present work, the rhythmic analysis of EEG utterances is carried out by filtering each channel into five frequency bands: Alpha (8–12 Hz), Beta (12–35 Hz), Theta (4–8 Hz), Delta (0.5–4 Hz), and Gamma (>35Hz). From the comparative sub-band analysis carried out in the open-access Correto database, the Alpha rhythmic bands are confirmed to have more vowel information compared to other subbands. In the context of classifying the severity level of EEG signals from epileptic patients, the Gamma band yielded the best performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electroencephalography

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Leveraging XGBoost and Clinical Attributes for Heart Disease Prediction 5

Leveraging XGBoost and Clinical Attributes for Heart Disease...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Susmitha, Kota Bhargavi, Maridu Sri, Achyuta Mohitha Sai Reddy, Bogala Devi Prasaad Satyanarayana, Paladugu Siva Rao, K V Ranga Vignan's Foundation for Science Technology and Research Department of Computer Science and Engineering Guntur India Department of Computer Science and Engineering-Data Science Hyderabad India

ISBN: (纸本)9798331530013

Despite the various advances in medical technology, heart disease continues to rank among the leading causes of mortality in the world, killing millions each year. There is hope that the risks involved with heart disease can be reversed in time and the outcomes of patients improved with earlier prediction and diagnosis. However, the use of conventional methods of diagnostic techniques tends to be very resource-intensive and inaccessible to other more disadvantaged areas. The present work defines a model for machine learning in predicting heart disease and illustrates a more efficient and accessible solution. There is a risk for heart disease that is determined using a set of various clinical features. The algorithm used in the prediction of heart disease is XG Boost that are applied to the dataset. The method provides a reliable tool for the early detection of heart disease, thus helping healthcare providers to improve preventive care and treatment strategies. The results of this study will be helpful in advancing research to identify other avenues for prediction of heart disease. © 2024 IEEE.

关键词： early diagnosis Heart disease UCI (University California Irvine) Heart Disease dataset XG Boost

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Integrating Machine Learning and Statistical Models in Enterprise Risk Analysis 4

Integrating Machine Learning and Statistical Models in Enter...

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

作者： Kalvala, Vikram Gupta, Arpita Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Hyderabad India

ISBN: (纸本)9798331523923

Companies in the complex financial world face several risks that require careful investigation and management. This critical review assesses various frameworks for large-scale financial risk analysis in organizations. Globalization, technological advancements, regulatory changes, and financial market interconnectedness pose significant challenges to traditional risk management methods. This report identifies and analyzes key frameworks, including quantitative models, qualitative assessments, scenario analysis, stress testing, and Monte Carlo simulations, evaluating their adaptability and usefulness across different organizational settings based on industry categorization, scale, complexity, and regulatory framework. This study highlights the strengths and weaknesses of each framework, with a particular focus on their applicability to market, credit, liquidity, operational, and strategic risks. Furthermore, it examines the incorporation of emerging risk factors such as climate change, geopolitical instability, cyber risks, and socio-economic developments into these frameworks. Advanced methodologies, including machine learning algorithms, artificial intelligence, and big data analytics, have shown potential in enhancing the precision and reliability of risk analysis, enabling firms to detect, quantify, and mitigate large-scale hazards effectively. The impact of regulatory frameworks such as Basel III, Solvency II, Dodd-Frank Act, and IFRS on financial risk analysis is also discussed, emphasizing the need for alignment with evolving compliance requirements. The results demonstrate the superiority of advanced models, such as Long Short-Term Memory (LSTM) networks, which achieved the highest accuracy (94%) and F1 Score (0.91), showcasing their effectiveness in handling sequential and temporal data. Random Forest models also performed robustly, with an accuracy of 92% and an F1 Score of 0.90, highlighting their capability for feature importance ranking. The Support Vecto

关键词： Decentralized finance

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Density Based Clustering Approach Combined with Fuzzy Logic for Elimination of Random Valued Impulse Noise

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Journal of The Institution of Engineers (India): Series B 2025年 1-14页

作者： Basu, Sulagna Bandyopadhyay, Aritra Department of Computer Science and Engineering Supreme Knowledge Foundation Group of Institutions 1 Khan Road Mankundu West Bengal Chandannagar712139 India

The reduction of impulse noise is crucial in processing pictures since it directly impacts the patterns of noise present. This paper proposes a two-step technique, known as DCIFF (DBSCAN clustering identified fuzzy filter), to effectively eliminate impulsive noise from digital images. The suggested DCIFF methodology employs the DBSCAN clustering algorithm to identify and classify noise. Subsequently, a noise reduction technique is presented, which relies on the principles of fuzzy reasoning. The density-based clustering approach DBSCAN is utilized to identify commonalities throughout all 5 × 5 sized windows that are created by centering each of the pixels in the picture. Next, to properly reduce noise, a sized 7 × 7 window is formed with the centre of each identified disturbed pixel. The non-noisy pixel intensities are multiplied by their triangle fuzzy membership values, and then the products are added together. The total result is then divided by the sum of the fuzzy membership of all the non-noisy pixels’ intensities. The outcome is utilized to eliminate the noise from the central pixel that is affected by noise. The DCIFF is assessed using standard image evaluation criteria for various pictures. On the Lena image modified by random valued impulsive noise at 80% noise density, DCIFF provided peak-signal-to-noise ratios (PSNR) of 25.86 dB and structural similarity indexes (SSIM) of 0.8429. In contrast to prior research on the same topic, the current study findings consistently demonstrate striking outcomes. © The Institution of Engineers (India) 2025.

关键词： Fuzzy logic

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An Initial Risk Assessment for Multimodal with LSTM-Based Trust Evaluation Framework for Autonomous Vehicle Security

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SN computer science 2025年第2期6卷 1-15页

作者： Renjith, P.N. Balasubramani, S. Ramesh, K. Patnala, Eswar School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India

This research aims to develop a new approach to increase the safety and reliability of Autonomous Vehicle (AV) through the proposed risk assessment framework, supported by the trust evaluation approach derived from a machine learning algorithm. This work proposes a comprehensive risk assessment model with real-time sensor data, Vehicle-to-Vehicle (V2V), key communication reliability, environmental cognition, and Improved Long Short-Term Memory (I-LSTM). This framework is multidimensional compared to most conventional models, focusing primarily on assessing individual threat factors. Here, the machine learning portion is designed to evaluate and predict trust by quickly processing vast amounts of information from sensors, signals, and prior operation history. Such an approach to trust assessment enables the system to continuously evaluate the dependability of automated vehicle operation and identify threats to security with improved accuracy. The effectiveness of the performance of this method is substantiated by a number of stand-alone and live scenarios, where the model's performance is higher in perceiving possible risks, avoiding threats, and enhancing safety than the conventional approaches. The findings suggest that the safety regulations of the AVs will be improved by using a multi-dimensional risk assessment model and integrating the I-LSTM trust rating with 95.5% accuracy, 96.7% risk identification, and 180 ms response time. This will cause AV and its surrounding environment to be accepted by the public and used on the roads. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Autonomous vehicles LSTM model Machine learning Risk assessment Trust evaluation

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Birch Clustering Approach for Detection of Random Valued Impulse Noise

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Journal of The Institution of Engineers (India): Series B 2024年 1-9页

作者： Basu, Sulagna Bandyopadhyay, Aritra Department of Computer Science and Engineering Supreme Knowledge Foundation Group of Institutions 1 Khan Road West Bengal Mankundu712139 India

computer vision relies on image processing for autonomous driving, surveillance, and medical imaging. Clustering, an unsupervised learning approach, is essential for picture data organization and smooth pre-processing. Photo noise has been removed using K-Means, K-Medoid, and Fuzzy C-Means clustering methods. K-means, K-Medoid, and Fuzzy C-Means may not cluster huge datasets well with limited memory or CPU. Traditional clustering methods struggle to accommodate running durations and quality as dataset quantities rise. Birch clustering, or Balanced Iterative Reducing and Clustering utilizing Hierarchies, is frequently used in image processing because of its scalability and efficiency. Hierarchies help BIRCH summarize the dataset while maintaining as much information as feasible. The smaller summary follows the larger dataset. BIRCH is often used alongside other clustering methods to compress the dataset for the next step. Birch clustering is scalable, efficient in high-dimensional spaces, and can handle enormous datasets. Birch clustering regularly builds a tree structure to arrange images into a hierarchy of sub-clusters for effective segmentation and representation. Birch clustering divides images into sections by examining pixel intensities or characteristics for image segmentation. Birch clustering identifies typical centroids inside clusters to simplify feature extraction and allow meaningful picture data displays. Its noise reduction and data distribution adaptability make it suited for many academic and industrial image processing tasks. Birch clustering's hierarchical tree structure allows for scalability, unlike k-means' centroids-based clusters. Birch clustering creates a hierarchical tree using image data sub-clusters and centroids. It handles massive datasets thanks to its efficient memory storage. It offers scalable and efficient clustering with decreased computing complexity by dividing and combining data to create a hierarchy. The detection capabilitie

关键词： Image segmentation

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computer-Aided Diagnosis-Based Grading Classification of Diabetic Retinopathy Using Deep Graph Correlation Network with IRF

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SN computer science 2024年第2期5卷 228页

作者： Poranki, Venkata Kotam Raju Srinivasarao, B. Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Diabetic retinopathy (DR) is a leading cause of blindness in different age groups. So, the early detection of DR can save millions of people from blindness issues. Further, the manual analysis of DR requires much processing time and experienced doctors. So, computer-aided diagnosis (CAD)-based artificial intelligence models were developed for early DR prediction. These traditional methods, however, needed to extract balanced deep features, resulting in poor classification performance. This study creates a CAD-based grading classification of DR using graph learning and optimal feature selection properties, which is named as CEFNet. The EyePACS and Messidor datasets are first put through the edited nearest neighbor oversampling (ENNOS) method, ensuring that each class's instances are equal. Then, a deep graph correlation network (DGCN) is used to get the unique features of each class by finding the connections in color eye fundus (CEF) images. In addition, an iterative random forest (IRF) is performed for feature selection to select the highly relevant DGCN features. Finally, these balanced optimal features are used to train a decision tree-based extreme gradient boosting (DTEGB) classifier, which then uses test CEF image data to make the prediction. The simulations done on the EyePACS and Messidor datasets show that the proposed CEFNet performed better than the current best DR grading classification methods. The EyePACS dataset had an accuracy of 99.50%, a sensitivity of 99.20%, and a specificity of 100%. The Messidor dataset had an accuracy of 99.80%, a sensitivity of 99.67%, and a specificity of 100%. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Diabetic retinopathy Extreme gradient boosting Graph convolutional neural network Iterative random forest Nearest neighbor oversampling

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A Comparative Analysis of Grape Plant Leaf Disease Detection - Methods and Challenges 5

A Comparative Analysis of Grape Plant Leaf Disease Detection...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Radhey, Nirujogi Sriram Chandra, Chennamadhavuni Manvith Abhiram, Velampudi Humesh, Ramasahayam Kumar, Munish Kumar, Sandeep Computer Science and Engineering Department Koneru Lakshmaiah Education Foundation A.P. Vaddeswarm India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation A.P. Vaddeswarm India

ISBN: (纸本)9798350361155

Grape leaf diseases significantly threaten global viticulture, leading to substantial declines in grape yield. These diseases, attributed to various factors such as bacteria, viruses, fungi, and pests like mealybugs, can propagate through diverse means, including wind, rain, and human activities. They identify symptoms such as leaf spots and yellowing for effective disease management. Current strategies for disease control involve biological agents and chemical pesticides, with challenges arising from the lack of treatment options for certain viral infections like grapevine fan leaf and rupestris stem pitting. This review explores the myriad methods employed for grape leaf disease detection, emphasizing the use of emerging technologies to address these challenges. Integrating IoT (Internet of Things) and image analysis has demonstrated efficacy in disease detection, enabling farmers to make informed decisions promptly. While fungicides prove effective against diseases like downy mildew and powdery mildew, viral infections present persistent challenges. Technological interventions, particularly IoT and image processing, offer promising avenues for identifying grape leaf diseases, providing farmers with timely and valuable information to enhance disease management strategies. The review underscores the importance of continued research and technological innovation to address the complexities of grape leaf diseases and ensure sustainable grape cultivation. © 2024 IEEE.

关键词： Disease control

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Disclosing the Potential of Deep Learning in Cassava Leaf Disease Analysis by using CNN and Neural Networks Approach 7

Disclosing the Potential of Deep Learning in Cassava Leaf Di...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Gopi, Arepalli Sudha, L.R. Iwin Thanakumar Joseph, S. Annamalai University Department of Computer Science & Engineering Tamilnadu Chidambaram India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Vaddeswaram India

ISBN: (纸本)9798350359299

The identification and classification of cassava infections are paramount due to their detrimental impact on agricultural productivity. This study conducts a comparative analysis to assess the effectiveness of various deep learning techniques in classifying cassava leaf diseases. Transformer-Embedded ResNet, EfficientNetV2 with visual attention, and a mobile-based model are explored to address the challenges posed by an imbalanced dataset. Leveraging deep Convolutional Neural Networks (CNNs) and attention mechanisms, these models exhibit enhanced precision and effectiveness. Evaluations measure accuracy, precision, recall, and F1-score, considering feasibility and computational complexity. Results indicate that the proposed models effectively control cassava infections compared to current methods. This study underscores the precision and efficacy of plant disease identification using deep learning techniques, presenting modern methodologies for thorough evaluation. With agriculture supporting a significant portion of the world's population, AI-powered automation offers solutions to production challenges. AI enhances agricultural precision, monitors crop health, identifies diseases, and forecasts weather conditions, particularly advantageous in addressing manpower shortages. Automated detection is crucial for mitigating agricultural disease risks and ensuring food security. Focusing on the examination of CNNs and neural networks, this study utilizes deep learning techniques to identify cassava leaf diseases using the Kaggle dataset and real-time photographs. Performance evaluation and enhancement are discussed, with a specific focus on the impact of disease on the Thai cassava crop. Experimental trials demonstrate the effectiveness of deep learning in automating cassava disease categorization, particularly in detecting brown streak virus illness, yielding notable F-measure and accuracy. This study advances disease classification and calls for further research in the

关键词： Crops

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