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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Kumar, B. Naveen Panduranga Rao, M.V. Sharan, Preeta Varun, K.R. Kallam, Suresh Kumaresan, M. Faculty of Engineering and Technology Department of Computer Science and Engineering Bengaluru India The Oxford College of Engineering Department of Electronics and Communication Engineering Bengaluru India The Oxford College of Engineering Department of Mechanical Engineering Bengaluru India

ISBN: (纸本)9789380544519

Optical biosensors, a novel invention at the intersection of biotechnology and optics, are revolutionizing the field of biomedical diagnostics. Optical biosensors act by identifying variations in light's wavelength, phase, polarization, or intensity caused by interactions between a sensing element and the target analyte. Optical biosensors outperform other sensor types in sensing applications due to their quick, accurate, and extremely sensitive results. When evaluating optical sensors, total power, core power, efficient area, and indices are the most crucial variables to consider. Recent years have seen notable advancements in machine learning (ML), a branch of artificial intelligence (AI). This review investigates the complementary nature of optical biosensors and machine learning methods. Here, the potential utility of ML for optical biosensors is explored in detail. The advantages and drawbacks of the most popular machine learning methods are listed based on an analysis of the optical biosensing data. It highlights the possible effects of machine learning on improving the sensitivity, selectivity, and adaptability of optical biosensing devices, and it summarizes current advancements in the field. Finally, the study considers the challenges and limitations that associated with current optical biosensing techniques. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning optical biosensor photonic crystal surface plasmon resonance surface-enhanced Raman scattering

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Forecasting Maternal Women's Health Risks using Random Forest Classifier 7

Forecasting Maternal Women's Health Risks using Random Fores...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Thakkar, Dhruvi Gandhi, Vaibhav C. Trivedi, Dhriti Faculty of Engineering & Technology Computer Science & Engineering Department Vadodara India Computer Engineering Department Anand India Computer Engineering Department Vadodara India

ISBN: (纸本)9798350359299

Nowadays, maternal health during pregnancy is a major concern, especially in rural areas where risks are increased by a lack of medical experts and poor infrastructure. The lack of effective methods for predicting maternal health risks poses a significant challenge in maternal healthcare. Traditional approaches often fall short of accurately identifying and managing complications during pregnancy, leading to adverse outcomes. To overcome, this new technique can accurately assess medical data in order to forecast threats to maternal health. This research investigates machine learning algorithms' potential in predicting such risks using medical data. The study utilizes a carefully curated dataset called the Maternal Health Risk Dataset, encompassing parameters like age, blood pressure, glucose levels, body temperature, heart rate, and risk level. Through ensemble learning-based feature engineering, algorithms like Random Forest, XG Boost, Support Vector Classifier (SVC), Decision Tree, and Logistic Multiclass are assessed. Results show Random Forest achieving an impressive accuracy of 94.26%, indicating its potential in maternal health risk prediction, crucial for minimizing adverse outcomes and enabling timely interventions during pregnancy. © 2024 IEEE.

关键词： Health risks

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An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

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Australian Journal of Electrical and Electronics engineering 2025年第1期22卷 105-120页

作者： Praveena Rachel Kamala, S. T, Saraswathi. Kaliraj, V. Nithya, A. Hema, R. Ramyadevi, R. Department of Information Technology Easwari Engineering college Tamil Nadu Chennai India Department of CSE S.A Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India Department of Computer Science and Applications SRM Institute of Science and Technology Tamil Nadu Chennai India

This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy. ©, Engineers Australia.

关键词： Long short-term memory

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Machine Learning and Clinical Insights Analysis of BMI Dataset Predictive Models 4

Machine Learning and Clinical Insights Analysis of BMI Datas...

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4th IEEE International Conference on Data engineering and communication Systems, ICDECS 2024

作者： Kadyan, Laksh Neerugatti, Vikram Taranum, Ayesha Rani, Geetha Somasekar, J. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350393354

Machine learning (ML) has developed at a superlative rate, accompanying requests spanning various fields. This research investigates the experience of strength data, exceptionally the request of machine learning (ML) algorithms to a Body Mass Index (BMI) dataset. The basic aim of searching out unwinds the dossier's many linkages and patterns, eventually chief to more thorough information of the variables deciding BMI. The study starts accompanying an initiation to the subject within reach, understood by a thorough study of appropriate work, a complex mechanics division, and an itemized reason of the reached results. However, because of advances in Machine Learning, we immediately have the talent to handle this issue in a more excellent manner. We've built an advance dossier-study system that can think a patient has diabetes, a suggestion of correction, admitting for early mediation. This predicting plan uses dossier analysis methods to extractable intuitions from a big number of diabetes-accompanying facts. Its basic aim is to correctly determine a patient's risk of diabetes. We've working categorization plans to a degree Decision Tree, Artificial Neural Networks (ANN), Naive Bayes, and Support Vector Machine (SVM) algorithms to cultivate the model. These outcomes show the influence of the subsystems in thinking diabetes risk admit a large size of veracity. This predictive finish can create a meaningful dissimilarity in labeling at-risk things early and providing bureaucracy with essential care and counseling before the ailment progresses. In summary, our machine intelligence-located scheme offers a natural still strong solution to call the risk of diabetes in subjects. By controlling the wherewithal of dossier reasoning and categorization algorithms, we can enhance early discovery and deterrent measures for this weighty affliction, eventually reconstructing patient consequences and reducing the burden of BMI-related complications. © 2024 IEEE.

关键词： Decision trees

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Achieving Secured Medical Network (SMN) through Stateless Mechanism and SkeyM in Medical-Internet of Things (M-IoT)

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Journal of engineering and Applied science 2024年第1期71卷 128页

作者： Nithya, S. Palanisamy, SatheeshKumar Nivethitha, T. Department of Computer Science and Engineering Kumaraguru College of Technology Tamil Nadu Coimbatore641049 India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bengaluru560064 India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore641202 India

Medical Internet of Things (M-IoT) synchronizes medical devices in a network to provide smart healthcare monitoring to doctors and to provide an interactive model for patients. This embedded networked system gained lots of importance in the last few years. Now almost every hospital adopts M-IoT. Though it had a lot of challenges in the initial stages of implementation such as feasibility, accuracy, and autonomy, now it has bridged the flaws with the help of several researchers in this field. But still, M-IoT lags behind in security and privacy aspects due to which attackers can intrude on the network and exploit patients’ health data. By examining the various intensive security threats imposed on M-IoT a unique way of handling and transferring data over the network is proposed in this paper. From our research findings, this would resolve the data security issues in M-IoT and commence the next advancements. © The Author(s) 2024.

关键词： Internet of things

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Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

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computers, Materials & Continua 2023年第5期75卷 4419-4444页

作者： Arwa Saif Fadel Osama Ahmed Abulnaja Mostafa Elsayed Saleh Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computer Sciences and EngineeringHodeidah UniversityYemen

Aspect-based sentiment analysis(ABSA)is a fine-grained *** fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely ***,most existing works on Arabic ABSA content separately address them,assume that aspect terms are preidentified,or use a pipeline *** solutions design different models for each task,and the output from the ATE model is used as the input to the APC model,which may result in error propagation among different steps because APC is affected by ATE *** methods are impractical for real-world scenarios where the ATE task is the base task for APC,and its result impacts the accuracy of ***,in this study,we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a *** paper integrates themulti-task model,namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification(LCF-ATEPC)and Arabic Bidirectional Encoder Representation from Transformers(AraBERT)as a shred layer for Arabic contextual text *** LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism(LCF)to capture the interactive information between an aspect and its ***,data augmentation techniques are proposed based on state-of-the-art augmentation techniques(word embedding substitution with constraints and contextual embedding(AraBERT))to increase the diversity of the training *** paper examined the effect of data augmentation on the multi-task model for Arabic *** experiments were conducted on the original and combined datasets(merging the original and augmented datasets).Experimental results demonstrate that the proposed Multi-task model outperformed existing APC *** results were obtained by AraBERT and LCF-ATEPC with fusion layer(AR-LCF-ATEPC-Fusion)and the proposed data augmentation

关键词： Arabic aspect extraction arabic sentiment classification AraBERT multi-task learning data augmentation

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Dynamics analysis and cryptographic implementation of a fractional-order memristive cellular neural network model

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Chinese Physics B 2024年第4期33卷 418-433页

作者：周新卫蒋东华 Jean De Dieu Nkapkop Musheer Ahmad Jules Tagne Fossi Nestor Tsafack 吴建华 Department of Information Engineering Gongqing CollegeNanchang UniversityJiujiang 332020China School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 511400China Department of Electrical Engineering and Industrial Computing University Institute of TechnologyDoualaCameroon Department of Computer Engineering Jamia Millia IslamiaNew Delhi 110025India Department of Physics Faculty of ScienceUniversity of YaoundeCameroon Electrical Engineering Department and Industrial Computing of ISTAMA University of DoualaDoualaCameroon

Due to the fact that a memristor with memory properties is an ideal electronic component for implementation of the artificial neural synaptic function,a brand-new tristable locally active memristor model is first proposed in this ***,a novel four-dimensional fractional-order memristive cellular neural network(FO-MCNN)model with hidden attractors is constructed to enhance the engineering feasibility of the original CNN model and its ***,its hardware circuit implementation and complicated dynamic properties are investigated on multi-simulation ***,it is used toward secure communication application *** it as the pseudo-random number generator(PRNG),a new privacy image security scheme is designed based on the adaptive sampling rate compressive sensing(ASR-CS)***,the simulation analysis and comparative experiments manifest that the proposed data encryption scheme possesses strong immunity against various security attack models and satisfactory compression performance.

关键词： cellular neural network memristor hardware circuit compressive sensing privacy data protection

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A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

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Neural Computing and Applications 2025年第6期37卷 4797-4821页

作者： Molaei, Reza Rahsepar Fard, Kheirollah Bouyer, Asgarali Department of Computer Engineering and Information Technology University of Qom Qom Iran Department of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, which plays a critical role in modern advertising. However, current solutions proposed for viral marketing are often impractical in real-world scenarios, and there has been limited investigation into viral marketing within the context of SIoT. To address this issue, we present a multi-objective integer linear programming model named ILP-ASoT that facilitates advertising through SIoT. Our approach introduces two realistic parameters: the node sociality rate and the node appropriateness rate as an advertising destination. By balancing the cost of selecting a seed set with the number of users who receive the advertisement, our model offers an appropriate trade-off. Since this is proven as an NP-hard problem, A PSO-based metaheuristic algorithm is proposed to provide near-optimal solutions for advertising in SIoT, called PSO-ASoT. Through simulations, we show that our PSO-ASoT algorithm outperforms state-of-the-art algorithms in several metrics, such as influence spread, advertising cost, and duplicated advertising rate. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Heuristic algorithms

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Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

HighTech and Innovation Journal

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HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

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Early detection of stroke disease using patients previous medical data instil with deep learning

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Multimedia Tools and Applications 2025年第16期84卷 16853-16881页

作者： Diwan, Tausif Gajbhiye, Saurav M. Goydani, Purva R. Gannarpwar, Vedant R. Khandait, Harshal R. Tembhurne, Jitendra V. Sahare, Parul Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur441108 India Department of Electronics and Communication Engineering Indian Institute of Information Technology Maharashtra Nagpur441108 India

Early detection of any disease and starting its treatment in this early stage are the most important steps in case of any life-threatening disease. Stroke is not an exception in this regard which is one of the leading causes of death and disability worldwide. We develop a simple but efficient deep neural network for the stroke prediction that accurately evaluates the probability of occurrence of stroke disease by treating this as a binary classification problem on one of the standard datasets named as Stroke Prediction Dataset available on Kaggle. With the help of effective pre-processing techniques such as SMOTE and FastICA on the noisy and imbalanced dataset, we could achieve improved performance for the binary classification of stroke prediction by employing a deep dense neural network. The architectural hyper-parameters are critically designed with the help of Keras Tuner and consisting of just ten layers in the deep dense neural network. With the help of this light weight deep dense model, we report an accuracy of 95%, AUC Score of 100% and F1-Score of 95% on a testing set. Moreover, we also present comparative illustration of various machine learning models for the aforesaid task along with the comparative illustration of various state of the arts with our proposed model for the stroke prediction. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Forecasting

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