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2nd International Conference on Power, Electrical, electronics and Industrial Applications, PEEIACON 2024

作者： Ahmmed, Tomal Tahsin Faruk, Md. Farukuzzarnan Srizon, Azmain Yakin Mahedy Hasan, S.M. Khansur, Md. Nasif Efat, Anwar Hossain Hossain, Md. Rakib Al Mamun, Md. Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331517984

Skin lesions are irregularities on the skin, often harmless, though some may progress to skin cancer. Detecting them early is crucial for successful treatment and prevention of cancer development. Traditional detection methods involve significant expenses and testing. This study introduces an auto-mated system for classifying skin lesions using a Shallow Tuned DenseNet (STDN), a compact Convolutional Neural Network (CNN) model. We employ Transfer Learning (TL) by fine-tuning a pretrained DenseNet-169 model with additional lightweight layers, allowing the STDN to adapt to the PH2 dataset's specific characteristics while remaining efficient. Initially, we utilize data augmentation techniques to address class imbalances within the dataset, improving model performance by reducing bias towards the majority class. The evaluation demonstrates a promising hundred percent accuracy with perfect F1-score and specificity. We attribute these results to effective transfer learning utilization, enhanced tuning, and partially to data augmentation, which mitigates overfitting and biases, facilitating robust feature extraction. Furthermore, we employ Grad-CAM (Gradient-weighted Class Activation Mapping) to visualize and interpret the model's predictions, enhancing trust and comprehension. The proposed STDN model makes a significant contribution by achieving high accuracy with a lightweight design, offering a potentially cost-effective and efficient solution for automated skin lesion detection, thereby aiding in early cancer prevention. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparison of Machine Learning and Neural Network Algorithms for an Automated Thai Essay Scoring 20

A Comparison of Machine Learning and Neural Network Algorith...

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20th International Joint Conference on computer Science and Software engineering, JCSSE 2023

作者： Suriyasat, Suttichai Chanyachatchawan, Sapa Tuaycharoen, Nuengwong Chulalongkorn University Department of Computer Engineering Thailand The National Electronics and Computer Technology Center Thailand

ISBN: (纸本)9798350300505

Thai students have relatively low scores on the reading literacy assessment conducted by PISA. Various studies reported that reading skills could be improved by writing. However, essay scoring is a time-consuming task. An automated essay scoring system can support both teachers and students by reducing the teachers' workload and providing predicted scores as feedback to students. A number of recent studies have focused on automated essay scoring dataset that contains only essays written in English. Little to no research has been done on the automated essay scoring system for the Thai language. The aim of this study is to develop a Thai essay scoring system using machine learning and deep learning models that have been reported to achieve good performance. We also try to improve the performance of our models by adding essay attribute features. The models that were used in this study are logistic regression, kNN, SVM, random forest, gradient boosting, XGBoost, LSTM (bag-of-words), LSTM (w2v), BERT-based, and LSTM+CNN (BERT embedding). The models were evaluated by six metrics, including accuracy, Quadratic weighted kappa, precision, recall, and F1-score along with 10-fold cross-validation. The experimental results show that XGBoost outperforms other models considering the majority of best metric scores in each set. For deep learning models with automatically extracted features from the text, the LSTM with word2vec features model yielded better performance than other deep learning models. © 2023 IEEE.

关键词： Students

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An Explainable Deep Learning Framework for Multi-Class Skin Lesion Classification While Resolving Class Imbalance 2

An Explainable Deep Learning Framework for Multi-Class Skin ...

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2nd International Conference on Power, Electrical, electronics and Industrial Applications, PEEIACON 2024

作者： Jahan, Israt Efat, Anwar Hossain Hasan, S. M. Mahedy Faruk, Md. Farukuzzaman Srizon, Azmain Yakin Hossain, Md. Rakib Al Mamun, Md. Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331517984

Skin lesions are indicative of various skin diseases, including serious conditions like melanoma, which can be life-threatening if not detected early. Automated systems for analyzing dermatoscopic images have shown promise in improving diagnostic accuracy. However, many existing studies rely on pre-trained convolutional neural network (CNN) architectures without customization, leading to potential biases and suboptimal performance. In this study, we propose a customized DenseNet121 architecture tailored for skin lesion classification. We address class imbalance issues through data augmentation and fine-tune the model to extract fine-grained features. Our method surpasses other cutting-edge models in terms of accuracy, precision, recall, specificity, sensitivity, and root mean square (ROC) values. Furthermore, we utilise Gradient-weighted Class Activation Mapping (Grad-CAM) to visually represent the decision-making process of the model. The next study will prioritise the validation of the model using a wide range of datasets and the integration of lesion-related information to enhance its performance. © 2024 IEEE.

关键词： Convolutional neural networks

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RenalNet: An End-to-End Hybrid Deep Learning and Ensemble Model for Accurate Kidney Disorder Classification 3

RenalNet: An End-to-End Hybrid Deep Learning and Ensemble Mo...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Haque, Fariha Siddique, Md Abu Ismail Shojol, Md. Shojeb Hossain Rajshahi University of Engineering & Technology Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350388282

Kidney disorders are a major public health issue affecting millions worldwide. Renal calculi and cancer are prevalent, causing chronic kidney disease(CKD). Artificial intelligence can assist in the precise diagnosis of kidney *** developed an end-to-end system using a custom CNN model to extract informative features from kidney images. Multiple ML classifiers provide complementary perspectives. A High-Performance Filter(HPF) selects the top classifiers for an ensemble model. The interactive web interface enables real-time prediction. Our approach achieves excellent performance metrics with an accuracy of 99.98%. Overall, We demonstrate an automated system combining deep learning and ensembling for the accurate characterization of diverse kidney abnormalities, which can aid in the early detection and treatment of CKD. © 2024 IEEE.

关键词： Classification (of information)

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Enhancing face recognition performance: a comprehensive evaluation of deep learning models and a novel ensemble approach with hyperparameter tuning

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Soft Computing 2024年第20期28卷 12399-12424页

作者： Selvaganesan, Jana Sudharani, B. Shekhar, S. N. Chandra Vaishnavi, K. Priyadarsini, K. Raju, K. Srujan Rao, T. Srinivasa Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R & ampD Institute of Science and Technology Avadi Tamil Nadu Chennai600062 India Guntur Dist. Andhra Pradesh Vadlamudi India Department of Electronics and Communication Engineering Sreenidhi Institute of Science and Technology Telangana Hyderabad501301 India Department of Computer Science and Engineering Sona College of Technology Tamil Nadu Salem636005 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Science and Engineering CMR Technical Campus Medchal Telangana Hyderabad501401 India Department of Electronics and Communication Engineering Aditya Engineering College Surampalem533437 India

In response to growing security concerns and the increasing demand for face recognition (FR) technology in various sectors, this research explores the application of deep learning techniques, specifically pre-trained Convolutional Neural Network (CNN) models, in the field of FR. The study harnesses the power of five pre-trained CNN models—DenseNet201, ResNet152V2, MobileNetV2, SeResNeXt, and Xception—for robust feature extraction, followed by SoftMax classification. A novel weighted average ensemble model, meticulously optimized through a grid search technique, is introduced to augment feature extraction and classification efficacy. Emphasizing the significance of robust data pre-processing, encompassing resizing, data augmentation, splitting, and normalization, the research endeavors to fortify the reliability of FR systems. Methodologically, the study systematically investigates hyperparameters across deep learning models, fine-tuning network depth, learning rate, activation functions, and optimization methods. Comprehensive evaluations unfold across diverse datasets to discern the effectiveness of the proposed models. Key contributions of this work encompass the utilization of pre-trained CNN models for feature extraction, extensive evaluation across multiple datasets, the introduction of a weighted average ensemble model, emphasis on robust data pre-processing, systematic hyperparameter tuning, and the utilization of comprehensive evaluation metrics. The results, meticulously analyzed, unveil the superior performance of the proposed method, consistently outshining alternative models across pivotal metrics, including Recall, Precision, F1 Score, Matthews Correlation Coefficient (MCC), and Accuracy. Notably, the proposed method attains an exceptional accuracy of 99.48% on the labeled faces in the wild (LFW) dataset, surpassing erstwhile state-of-the-art benchmarks. This research represents a significant stride in FR technology, furnishing a dependable and accurate

关键词： Convolutional neural networks

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Braille–Latin conversion using memristive bidirectional associative memory neural network

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12511-12534页

作者： Vaidyaraman, Jayasri Thyagarajan, Abitha K. Shruthi, S. Ravi, V. School of Electronics Engineering Vellore Institute of Technology Chennai India Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States

Artificial neural networks (ANNs) are finding increasing use as tools to model and solve problems in almost every discipline in today’s world. The successful implementation of ANNs in software—particularly in the fields of deep learning and machine learning—has spiked an interest in designing hardware architectures that are custom-made to implement ANNs. Several categories of ANNs exist. The two-layer bidirectional associative memory (BAM) is a particular class of hetero-associative memory networks that is extremely efficient and exhibits good performance for storing and retrieving pattern pairs. The memristor is a novel hardware element that is well-suited to modelling neural synapses because it exhibits tunable resistance. In this work, in order to create a device that can perform Braille–Latin conversion, we have implemented a circuit realization of a BAM neural network. The implemented hardware BAM uses a memristor crossbar array for modelling neural synapses and a neuron circuit comprising an I-to-V converter (resistor), voltage comparator, D flip-flop, and inverter. The efficiency of the implemented hardware BAM was tested initially using 2 × 2 and 3 × 3 patterns. Upon successfully verifying the ability of the implemented BAM to store and retrieve simple pattern pairs, it was trained for a pattern-recognition application, namely mapping Braille alphabets to their Latin counterparts and vice versa. The performance of the implemented BAM network is robust even on the introduction of noise. The application can recognize the input patterns with accuracies of 100% in either direction when tested with up to 30% noise. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Neural networks

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Synergizing Futures: Precision Career Mapping with Llama 2 and AI Fine-Tuning for Personalized Path Prediction and Guided Navigation

Synergizing Futures: Precision Career Mapping with Llama 2 a...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Singh, Anuradha Sharma, Himanshu Jindal, Kanika Chaudhary, Ankur Noida Institute Of Engineering And Technology Department Of Computer Science And Engineering Greater Noida India Noida Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Greater Noida India

ISBN: (纸本)9798350366846

These days, a lot of students struggle in how to choose the career. As they progress through their studies, students must recognize their abilities and assess their areas of interest to determine the most appropriate job for them. With the aid of this approach, today's youngsters will be able to determine which career route will yield the best outcomes in the long run if they choose the suggested vocation. This will assist in raising the student's performance and spark their interest to keep them concentrated on their desired job. This system is based on an exam that students must complete;based on their responses, it will produce a summary of the test results. The primary goal of this system is to give a summary of the artificial intelligence methods that we employed to forecast the student's performance. This structure likewise be emphasizing how we are identifying characteristics in student data by employing prediction systems. For educators, educational institutions, and students alike, using this system has proven to be advantageous. In this paper we have used various technologies like Flutter, Llama-2 generative A.I. model, Firebase and UI/UX design tools. Our experimental results of proposed system reduce the career searching effort by 80% when tested for Lyman people. © 2024 IEEE.

关键词： Students

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Secure electronics Medical Infrastructure for Healthcare 4.0: A Voice Identity Management-Based Approach 2

Secure Electronics Medical Infrastructure for Healthcare 4.0...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Pal, Prashnatita Sahana, Bikash Chandra Poray, Jayanta Department of Electronics & Communication Engineering National Institute of Technology Patna India Department of Electronics & Communication Engineering National Institute of Technology Patna India Department of Computer Science & Engineering Techno India University Kolkata India Department of Electronics & Communication Engineering St Thomas College of Engineering & Technology Kolkata India

Securing Electronic Medical Records for Healthcare 4.0 is one of the prime requirements in the medical care service domains. Healthcare 4.0 refers to the evolution process of healthcare systems that leverage digital technologies, connectivity, and data-driven insights to provide more personalized and efficient care. With the increasing adoption of electronic healthcare records and the growing importance of data in the healthcare sector, ensuring the privacy of patient data becomes paramount. To ensuring the security for healthcare 4.0 in the current work a voice authentication-based system is devised. This enables the protection of electronic healthcare records (EHRs) in the association of healthcare 4.0. More precisely here the voice authentication is used as an additional layer of security for accessing and managing EHRs. The Voice authentication can also be categorized as the voice biometric. This is a technology that uses unique voice characteristics and patterns to verify the identity of individuals. The result establishes the innovative smart security arrangement for the Healthcare 4.0 using the voice authentication-based identity management infrastructure;and a modified version of asymmetric cryptosystem has been used inside the blockchain. The simulation outcome indicates better performance in the form of block generation time, block number, ensure the transaction, and minimize the latency of the transaction. © 2024 Elsevier B.V.. All rights reserved.

关键词： Blockchain

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Deep convolutional spiking neural network fostered automatic detection and classification of breast cancer from mammography images

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Research on Biomedical engineering 2023年第4期39卷 833-841页

作者： SenthilPrakash, T. Kannan, G. Prabhakaran, Salini Prajapati, Bhagirath Parshuram Department of Computer Science and Engineering Shree Venkateshwara Hi-Tech Engineering College Tamil Nadu Gobichettipalayam India Department of Electronics and Communication Engineering Amrita College of Engineering and Technology Nagercoil India Department of Computer Science and Engineering Puducherry Technological University Puducherry India Department of Computer Engineering A. D. Patel Institute of Technology Gujarat New Vallabh Vidyanagar India

Purpose: Breast cancer (BC) is a leading cause of death among women. Several existing methods on breast cancer classification were suggested with machine learning, but none of them accurately classify the breast cancer and requires high computational time. To overcome these problems, deep convolutional spiking neural network fostered automatic detection and classification of breast cancer from mammography images (DCSNN-BCDC-MI) is proposed in this manuscript. Methods: Initially, input breast mammography pictures are taken from Digital Database for Screening Mammography (DDSM) data set. The input breast mammography images are pre-processed and feature extracted. The DCSNN is employed to categorize pictures using retrieved characteristics, such as benign and malignant. Results: The proposed DCSNN-BCDC-MI technique is implemented at PYTHON. The performance of the proposed DCSNN-BCDC-MI technique attains 31.13%, 12.78%, 14.38%, 19.33%, and 19.11% high accuracy and 61.08%, 66.42%, 66.54%, 50.26%, and 53.03% low computation time comparing to the existing techniques, like deep learning fed capsule neural network for breast cancer detection utilizing mammogram images (OMLTS-DLCN-BCDC-MI), extended model for tumor location detection based on deep learning (VGG-CNN-BCDC-MI), deep learning algorithm for automated identification with breast cancer classification depending on transfer-learning strategy (VGG16-BCDC-MI), deep learning method for breast cancer identification on mammography by learning certain cancerous mass representations (CNN-Deep AE-BCDC-MI), and automatic identification with mammograms classification dependent on increased extreme learning machine along deep learning (ICS-ELM-BCDC-MI), respectively. Conclusion: The simulation result exemplifies that the proposed method can be capable to determine the optimum global solutions efficiently with the exact existing methods. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical Enginee

关键词： Mammography

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RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

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computers, Materials & Continua 2024年第12期81卷 4833-4851页

作者： Weiwei Hu Kiran Sree Pokkuluri Rajesh Arunachalam Bander A.Jabr Yasser A.Ali Preethi Palanisamy School of Information Engineering Technology&MediaUniversity of HenanKaifeng475004China Department of Computer Science and Engineering Shri Vishnu Engineering College forWomenBhimavaram534202Andhra PradheshIndia Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia

Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding *** sensor nodes are responsible for accumulating and exchanging ***,node local-ization is the process of identifying the target node’s *** this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization ***,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D *** position of the anchor nodes is fixed for detecting the location of the ***,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node *** the regular and irregular surfaces,this hybrid algorithm effectively performs the localization *** suggested hybrid algorithm converges very fast in the three-dimensional(3D)*** accuracy of the proposed node localization process is 94.25%.

关键词： Sensor node localization received signal strength indicator 3D wireless sensor network deep neural network average localization error and hybrid T cell immune with lotus effect optimization algorithm

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