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Deep learning based RAGAE-SVM for Chronic kidney disease diagnosis on internet of health things platform

Multimedia Tools and Applications 2024年 1-39页

作者： Kandukuri, Prabhakar Abdul, Ashu Kumar, Kuchipudi Prasanth Sreenivas, Velagapudi Ramesh, G. Gundu, Venkateswarlu Department of Artificial Intelligence and Machine Learning Chaitanya Bharathi Institute of Technology Telangana Hyderabad500075 India Department of Computer Science and Engineering SRM University Andhra PradeshNeerukonda Mangalagiri 522503 India Department of Computer Science & amp Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India Department of Computer Science and Engineering SRK Institute of Technology Andhra Pradesh Vijayawada521108 India Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology TelanganaBachupally Hyderabad500090 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra PradeshVaddeswaram 522302 India

Chronic kidney disease (CKD) is a prominent disease that causes loss of functionality in the kidney. Doctors can now more easily gather patient health status data due to the growth of the Internet of Health Things (IoHT). The IoHT data contains a huge number of redundant data, making it challenging to predict CKD disease quickly and accurately. In healthcare applications like feature-based classification, a variety of disease diagnosis systems were used to address this problem. Current disease detection algorithms suffer from imbalanced dataset processing, low-accuracy feature learning, and high computational power requirements. Thus, deep learning-based clinical decision support systems have been developed to solve these complexities. To remove outliers from medical data, data collected with IoHT devices is first pre-processed using an enhanced K-means clustering technique. The Synthetic Minority over Sampling Technique is used to balance data because the IoHT dataset is highly imbalanced. The classifier detects anomalies in less time because of the implemented processing step. The balanced CKD dataset is then presented for use with a novel classifier called Residual Attention-Gated Autoencoder with Support Vector Machine. In order to improve accurate detection, the adopted classifier can learn and extract features. The proposed method results in an increased accuracy of 99.43% within 70.1 s of computation time. The mean intersection of the union and kappa coefficient of the proposed method is 98.15% and 98.1%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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Global-local term fusion for optimised community Q&A topic modelling

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International Journal of Innovative Computing and Applications 2024年第1期15卷 50-69页

作者： Prabha, Sneh Sardana, Neetu Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Community question and answer (Q&A) websites have become invaluable information and knowledge-sharing sources. Effective topic modelling on these platforms is crucial for organising and navigating the vast amount of user-generated content. To address these challenges, we propose a novel global-local term fusion with optimised community (GLOCOM) Q&A topic modelling approach that leverages both local and global term importance to enhance topic modelling on community Q&A websites. GLOCOM combines term frequency-inverse document frequency for local importance and entropy for global importance. Further, we employ fuzzy clustering to enhance the representation of multifaceted topics. Furthermore, clustering results are optimised using a genetic algorithm (GA) to refine cluster assignments and centroids. We compared the proposed model with baseline models LDA and FLSA. GLOCOM has performed consistently well for all topic numbers. It has shown an improvement of 8.86% in silhouette score as compared to LDA and excelled for datasets with size > 3 MB. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Inverse problems

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BGRU-MTRA: bilinear GRU networks with multi-path temporal residual attention for suspicious activity recognition

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Neural Computing and Applications 2025年第1期37卷 185-212页

作者： Pandey, Ajeet Kumar, Piyush Computer Science and Engineering National Institute of Technology Patna Ashok Rajpath Bihar Patna800005 India

Suspicious activity recognition (SAR) is an active research field in computer vision and image processing due to the rapid demand for intelligent video surveillance systems. However, current automated systems focus too much on the temporal dynamics of events in videos and ignore the importance of spatial dynamics. The existing methods for SAR are complex and demand substantial resources. Hence, this paper proposes a novel trade-off architecture for SAR integrating the strengths of the multi-path temporal residual attention (MTRA) module with the bilinear-gated recurrent unit (BGRU) module. The MTRA module combines spatial feature extraction (SFE) with temporal residual attention network (TRAN) blocks for improving SAR by enhancing resilience to variations in object sizes, viewpoints, and motion patterns. It selects relevant action features, addresses vanishing gradient issues, and reduces spatial dimension. The BGRU module preserves spatial features, improving the model’s ability to recognize fine-grained features and complex temporal patterns for effectively recognizing suspicious activities. The BGRU-MTRA system yields recognition accuracy of 93.28%, 98.97%, 99.86%, and 99.66%, and 48.42% on the benchmark hybrid crime action (HCA), UT interaction (UTI), CAVIER, hockey fight (HF), and the most challenging UCF-crime datasets, respectively. Through comprehensive experiments, it is clear that the proposed method outperforms state-of-the-art methods over benchmark datasets with reduced parameters. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Spatio-temporal data

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An Exploration of Deep Learning Techniques for the Detection of Grape Diseases

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Recent Advances in computer science and Communications 2024年第2期17卷 1-12页

作者： Pandey, Kavita Chandak, Abhimanyu Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Plant diseases are one of the major contributors to economic loss in the agriculture industry worldwide. Detection of disease at early stages can help in the reduction of this loss. In recent times, a lot of emphasis has been done on disease detection due to the overall increase in production as well as the loss of grape number. With deep learning, having a promising future and having the advantages of automatic learning and feature extraction, the use of these techniques has now been widely spread. This paper reviewed the existing deep-learning techniques available for grape disease detection. Firstly, covering the various steps in a grape disease detection model ranging from the various sources of image acquisition, the different image augmentation techniques and the various models used, and the parameters required to evaluate. Secondly, the study summarizes the im-portant findings of all literature available on the theme. The paper also tries to highlight the various challenges faced by the researchers and the common trend among them, so that future research on the topic can achieve higher performance. © 2024, Bentham science Publishers. All rights reserved.

关键词： Losses

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Classification of Pancreatic Cancer using Kolmogorov-Arnold Networks: An Upgrade on Traditional Machine Learning for Medical Classification 6

Classification of Pancreatic Cancer using Kolmogorov-Arnold ...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Vinod, Ratnaparkhi Gaurang Kumar, Manemoni Arun Chowdary, Chinthalapudi Vaahnitha Suvarchala, K. Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798331522667

Pancreatic cancer is one of the most fatal cancers, and medical doctors basically face major problems in its early diagnosis. This article throws light on the work involving the application of Kolmogorov-Arnold Networks (KANs), where we deploy a new machine learning paradigm in the diagnosis of pancreatic cancer. The process of classification basically happens in pancreatic cancer using urinary biomarkers. Unlike Logistic Regression and XGBoost, models have struggled with complex and non-linear medical data, KANs can manage large and complex datasets with ease. Using a dataset of urinary biomarkers, trained a KAN model with EBM techniques for feature selection to increase the model's interpretability and feature relevance. The model presented in this paper showed superior performance compared to traditional models in terms of high accuracy and generalization capability. KANs therefore will be very essential in handling efficiently the challenge presented by the vastness of data in medical studies regarding pancreatic cancer detection. Findings from the studies indicate a better potential in reliable use for progression in developing improved detection of pancreatic cancer using the KAN method. The publication contributes to current research that makes innovative uses in machine learning a reality for improvements in diagnosis as well. © 2025 IEEE.

关键词： Diseases

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Evaluation of Routing Protocol Performance for Enhanced Operations of Unmanned Aerial Vehicles (UAVs) 2

Evaluation of Routing Protocol Performance for Enhanced Oper...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Gupta, Vinti Yadav, Devendra Kumar Agarwal, Megha Srm Institute of Science and Technology Computer Science and Engineering Ghaziabad India Xim University Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350372847

Unmanned Aerial Vehicles (UAVs) are airborne nodes that are controlled remotely from ground stations. They have been used in a variety of applications in recent years, including disaster management, military operations, search and rescue, ecological study, and others. Among the advancements, the notion of Flying Ad-hoc Networks (FANETs) stands out. FANET, functions remotely like Mobile Ad-hoc Networks (MANETs) and Vehicular Ad-hoc Networks (VANETs), are an advancement over standard networking systems. Ad-hoc networks are necessary for both civilian and military operations, demanding a variety of support systems. In Ad-hoc Networks, data is always sent under all situations, which is required for seamless communication across all UAVs. In contrast, the complex dynamics and ever-changing topology of UAV networks make it difficult for researchers to integrate efficient routing procedures into FANETs. The accomplishment of UAV routing procedures is related to the total network's success rate. Producing a routing system capable of cost- effective and reliable data transfer in such a difficult environment is a difficult assuming. In this study, we look at routing protocols using the NS3 simulator. In this research, we investigate routing protocols using the NS3 simulator. The study looks at the effectiveness of several UAV routing protocols, including Ad-Hoc On-Demand Distance Vector (AODV), Destination Sequenced Distance Vector (DSDV), and Optimized Link State Routing (OLSR). These protocols' performance is evaluated using a variety of measures, including Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Throughput, End-to-End Delay, and Jitter Delay. The findings estimation the effectiveness of routing processes in the presence of varying UAV speeds. This research carefully explores how different routing protocols function under varied UAV intensities and speeds. © 2024 IEEE.

关键词： Routing protocols

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Role of Artificial Intelligence and Deep Learning in Skin Disease Prediction: A Systematic Review and Meta-analysis

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Annals of Data science 2024年第6期11卷 2109-2139页

作者： Nancy, V. Auxilia Osvin Prabhavathy, P. Arya, Meenakshi S. Computer Science and Engineering College of Engineering and Technology SRM Institute of Science and Technology Vadapalani Tamil Nadu Chennai India Institute of Transportation IOWA State University IowaIA United States

Skin is a most essential and extraordinary part of the human structure. Exposure to chemicals such as nitrates, sunlight, arsenic, and UV rays due to pollution and depletion of the ozone layer is causing various skin diseases to spread rapidly. Digital healthcare offers many opportunities to reduce time, and human error, and improve clinical outcomes. However, the automatic recognition of skin disease is a major challenge due to high visual similarity between different skin diseases, low contrast, and large inter variation. Early detection of skin cancer can prevent death. Thus, Artificial intelligence (AI) and Machine Learning (ML) helps the physicians to improve clinical judgment or change manual perception. For skin cancer diagnostics, the ML/AI algorithm can outperform or match professional dermatologists in multiple studies. Different pre-trained architectures such as ResNet152, AlexNet, VGGNet, etc. are used for fusing different skin disease features such as texture, color, etc. and they are also utilized for conducting segmentation tasks. The variations in reflection, lesion size, shape, illumination, etc. often make automatic skin disease classification a complex task. ISIC 2019 and HAM 10000 are the widely used public datasets for skin disease prediction. More technical paper on skin cancer diagnosis is compared in this study. This report examines the majority of technical papers published between 2018 and October 2022 in order to appreciate current trends in the disciplines of skin cancer prediction. A study that combined clinical patient data with deep learning models (DL) increased the accuracy of predicting skin cancer. This article presents a visually attractive and well-organized summary of the current study findings. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Deep learning

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Dynamic-EC:an efficient dynamic erasure coding method for permissioned blockchain systems

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Frontiers of computer science 2025年第1期19卷 15-28页

作者： Mizhipeng ZHANG Chentao WU Jie LI Minyi GUO Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200200China Yancheng Blockchain Research Institute Hengyang 421200China

Blockchain as a decentralized storage technology is widely used in many *** has extremely strict requirements for reliability because there are many potentially malicious ***,blockchain is a chain storage structure formed by interconnecting blocks1),which are stored by full replication method,where each node stores a replica of all blocks and the data consistency is maintained by the consensus *** decrease the storage overhead,previous approaches such as BFT-Store and Partition Chain store blocks via erasure ***,existing erasure coding based methods utilize static encoding schema to tolerant f malicious nodes,but in the typical cases,the number of malicious nodes is much smaller than f as described in previous *** redundant parities to tolerate excessive malicious nodes introduces unnecessary storage *** solve the above problem,we propose Dynamic-EC,which is a Dynamic Erasure Coding method in permissioned blockchain *** key idea of Dynamic-EC is to reduce the storage overhead by dynamically adjusting the total number of parities according to the risk level of the whole system,which is determined by the number of perceived malicious nodes,while ensuring the system *** demonstrate the effectiveness of Dynamic-EC,we conduct several experiments on an open source blockchain software *** results show that,compared to the state-of-the-art erasure coding methods,Dynamic-EC reduces the storage overhead by up to 42%,and decreases the average write latency of blocks by up to 25%,respectively.

关键词： blockchain Byzantine Fault Tolerance(BFT) erasure coding consensus reputation evaluation

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Knowledge Graph Representation Learning based on Entity Semantic Distance Classification

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computer-Aided Design and Applications 2024年第S7期21卷 256-269页

作者： Zhang, Yi Cao, Wanhua Liu, Juntao Wang, Yuanbin Rao, Ziyun College of Computer Science and Technology Harbin Engineering University Harbin China Wuhan Digital Engineering Research Institute Wuhan China

In recent years, significant progress has been made in knowledge graph representation learning, which has shown promising results in knowledge computing applications such as relation extraction and knowledge reasoning. However, the unbalanced distribution of relations and entities in knowledge graphs leads to low training efficiency. To address this issue, this paper proposes a novel knowledge representation learning method based on entity distance classification. This method classifies entities based on their semantic distance on a specific relational plane, and employs different training strategies to increase the training opportunities for entities with low semantic distance differentiation. Moreover, the loss function is adjusted by introducing different residual weights, which allows for the allocation of different training opportunities to negative samples. The effectiveness of our approach is demonstrated by comparing it with mainstream knowledge representation models on various benchmark datasets. © 2024 U-turn Press LLC,.

关键词： Knowledge graph

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Cellular Automaton–Based Sentiment Analysis for Bipolar Classification of Reviews

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Complex Systems 2025年第1期34卷 29-57页

作者： Elizabeth, M.J. Chaudhuri, Parimal Pal Hazari, Raju Department of Computer Science and Engineering National Institute of Technology Kerala Calicut India Indian Institute of Technology Kharagpur India

One of the main goals of sentiment analysis is to analyze human perception to continuously adapt to each person’s demands. The information gathered is structured to understand the mood or emotional tone of the reviews and comments. In every aspect of society, there is always an opportunity for opinions and suggestions from the public. So, the quality of the services provided by various resources can considerably increase if we can determine whether those thoughts are positive, negative or neutral. In this paper, we employ cellular automata (CAs) to categorize the reviews as positive or negative. The fundamental advantage of the suggested technique is that there is less need to be concerned about the linguistic characteristics of human languages. The proposed method performs better by leveraging a variety of parameters that are extracted by analyzing cycle length (CL) graphs generated using CL signals produced by elementary CAs (ECAs) without employing any form of pre-trained language model. We evaluated the effectiveness of the proposed cellular automaton–based machine learning (CAML) classifier by assessing certain parameters such as recall, precision, F1 score and accuracy. Our method achieves a maximum accuracy of 92.44% for the sentiment tweet dataset. We compared the proposed technique with all existing machine learning models, as well as recent methodologies employed in the field. The suggested method performs well in predicting the positive and negative classes of sentiments using CAs. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： Emotion Recognition

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