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Improving Student Performance Prediction Using a PCA-based Cuckoo Search Neural Network Algorithm 27

Improving Student Performance Prediction Using a PCA-based C...

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27th International Conference on Knowledge Based and Intelligent information and engineering Sytems, KES 2023

作者： Ali, Maria Liaquat, Muhammad Daniyal Atta, Muhammad Nouman Khan, Abdullah Lashari, Saima Anwar Ramli, Dzati Athiar Institute of Computer Science and Information Technology The University of Agriculture Peshawar Pakistan College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia School of Electrical and Electronic Engineering University Sains Malaysia USM Engineering Campus Nibong Tebal Pulau Pinang14300 Malaysia

The ANN is a commonly used network for pattern recognition, and has been trained for various tasks such as prediction, classification, and engineering. However, this model faces challenges such as local minima and slow convergence, which have been addressed through different strategies such as combining the artificial neural network (ANN) with optimised models like the cuckoo search (CS) algorithm. However, for large datasets, the hybrid ANN-based CS algorithm can lead to overfitting. To overcome this issue, the authors propose a new algorithm called Principal Component Analysis with Cuckoo Search Neural Network (PCACSNN). The performance of this algorithm is compared to other commonly used algorithms such as ANN, backpropagation neural network (BPNN), and cuckoo search backpropagation (CSBP), using the Mean Square Error (MSE) and accuracy on classification problems. The simulations were performed on the Student Performance dataset taken from the UCIMLR. The results show that the proposed model performs better than the other models, achieving high accuracy and low MSE for both mathematics and Portuguese student datasets. For the mathematics students, the suggested model attained an accuracy of (99.32%) with MSE of 2.77E-07 for 70% training data and an accuracy of 98.52% with MSE of 2.50E-04 for 30% training data. Similarly, for the Portuguese student dataset, the proposed model obtained (99.38%) accuracy with MSE of 1.09E-08 for 70% training data and 98.72% accuracy with MSE of 1.01E-04 for 30% training data. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Accuracy Artificial Neural Network (ANN) Back Propagation Cuckoo Search (CS) Algorithm Local Minima Mathematics Mean Square Error (MSE) Portuguese Slow Convergence

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Training Learning Weights of Elman Neural Network Using Salp Swarm Optimization Algorithm 27

Training Learning Weights of Elman Neural Network Using Salp...

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27th International Conference on Knowledge Based and Intelligent information and engineering Sytems, KES 2023

作者： Mahdi, Qaedah Ali Musaeed Naji Ali, Maria Atta, Muhammad Nouman Khan, Abdullah Lashari, Saima Anwar Ramli, Dzati Athiar Institute of Computer Science and Information Technology The University of Agriculture Peshawar Pakistan College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia School of Electrical and Electronic Engineering University Sains Malaysia USM Engineering Campus Pulau Pinang Nibong Tebal14300 Malaysia

Elman Neural Network (ENN) is one of the most common type of recurrent neural network (RNN). It is frequently applied to numerous applications such as classification and predication. However, the ENN model, suffers from serious flaws like network stagnancy and delayed convergence during training. To enhance the effectiveness of the ENN and address the mention problems, numerous studies have been conducted to speed up the learning process of ENN. Many optimization techniques are being used and each algorithm has its own characteristics in terms of efficiency. Some of the algorithms inspire from nature, while others come from the swarm's combined behaviour. Different numbers of optimization methods, including Particle Swarm Optimization (PSO), Bat Algorithm, and Cuckoo Search (CS) are applied to improve the ENN's learning process. As a result, this study suggested using Salp Swarm algorithm (SSA) to address these issues and speed up the ENN algorithm's learning process. The SSA, which is based on the typical behaviour of Salp swarms, is proposed in this research as a novel optimization technique. Due to its effectiveness and Salp intelligent behaviour, a novel Salp algorithm improves the learning process of the artificial neural network (ANN), and ENN models. Therefore this study proposed Salp swarm artificial neural network (SSANN) and Salp swarm Elman neural network (SSElmanNN). Performance of the suggested models is evaluated against ANN, back propagation neural network (BPNN), along with ENN in term of accuracy and Mean Square Error (MSE). Two datasets such as IRIS and Credit Card are used for simulation. The simulation results demonstrate that the suggested models outperform the other algorithms used in this study in terms of MSE and accuracy. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： and Credit Card ANN BPNN ENN Iris MSE SSA

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Cloud Server Based Intelligent Health Care Kit using Body Sensor Network 2

Cloud Server Based Intelligent Health Care Kit using Body Se...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Arul Prasath, A. Ranganathan, Chitra Sabapathy Saravanan, K. Srinivasan, S. Latha, N. Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Mphasis Corporation ChandlerAZ United States Department of Information Technology R.M.D Engineering College Tamil Nadu Chennai India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

In this study, the model investigates designing and deploying a Body Sensor Network (BSN)-based intelligent healthcare kit hosted on a cloud server. This study aims to determine whether and how well this technology can track and manage people's health. The study focuses on developing and implementing wearable sensors for continuously monitoring vital signs such as heart rate, blood pressure, temperature, and oxygen saturation. The data is sent wirelessly to a gateway device or central hub, which may be processed locally or analyzed in the cloud using sophisticated algorithms and machine learning methods. The cloud server is crucial for tracking users' vitals, spotting outliers, and alerting users and medical staff in real time. Based on an individual's health data, the system offers suggestions for changing one's lifestyle, taking medications as prescribed and engaging in physical activity. The cloud server ensures the privacy and security of stored data and complies with applicable data protection laws. In addition, methods are investigated for integrating the intelligent healthcare kit with preexisting healthcare systems and electronic health records to support the smooth exchange of data and the provision of all-encompassing medical treatment. This work aims to promote healthcare technology and patient care by conducting empirical research and assessing a cloud server-based intelligent healthcare kit employing a body sensor network. © 2023 IEEE.

关键词： Body Sensor Network Cloud Server Database Health Management Healthcare Kit

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Graph Neural Networks for Resource Allocation Optimization in Healthcare Industry 2

Graph Neural Networks for Resource Allocation Optimization i...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Manivannan, S.K. Kavididevi, Venkatesh Muthukumaran, D. Nandhini, R. Vakula, V. Srinivasan, C. Srm Institute of Science and Technology Faculty of Management Kattankulathur Tamil Nadu Chennai India Vardhaman College of Engineering Department of Information Technology Telangana Hyderabad India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sri Sai Ranganathan Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Optimizing the allocation of healthcare resources has never been easier than with the help of Graph Neural Networks (GNNs). In healthcare systems, where complications abound, GNNs may help with the allocation of resources in a way that improves both patient outcomes and operational efficiency. The goal is to use GNNs' graph-modelling capabilities to describe healthcare networks, with hospitals, doctors, and other medical resources acting as nodes and potential transfers of patients or shared resources as edges. Using both past and present data, the goal is to optimize resource allocation by forecasting the most effective distribution patterns. The decision-making process is improved by using important methods including attention mechanisms, message-passing algorithms, and node embeddings. The accuracy and scalability of healthcare resource management are both enhanced by GNNs, which capture spatial and temporal dynamics. The general efficacy of healthcare delivery systems is improved by this method, which permits more precise forecasts and efficient use of resources. The results highlight how GNNs can change healthcare resource allocation models. From GitHub repository data the short interfering RNAs (siRNAs) and microRNAs (miRNAs) data with efficacy is calculated. Out of the 8 data collected as samples for various siRNAs the efficacy minimum value is 0.07 and the maximum value is 0.946 and in other data samples collected it is 0.386 and 0.777. For miRNAs it is 0.934 and 0.394 and in other data sample collected it is 0.738 and 0.432. © 2024 IEEE.

关键词： Graph neural networks

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Machine Speech Chain with Emotion Recognition

Machine Speech Chain with Emotion Recognition

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International Conference of Advanced informatics: Concept, Theory and Application (ICAICTA)

作者： Akeyla Pradia Naufal Dessi Puji Lestari Ayu Purwarianti Kurniawati Azizah Dipta Tanaya Sakriani Sakti School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Faculty of Computer Science University of Indonesia Depok Indonesia Graduate School of Information Science Nara Institute of Science and Technology Nara Japan

ISBN: (数字)9798331520311

ISBN: (纸本)9798331520328

Developing natural speech recognition and speech synthesis systems requires speech data that authentically represents real emotions. However, this type of data is often challenging to obtain. Machine speech chain offers a solution to this challenge by using unpaired data to continue training models initially trained with paired data. Given the relative abundance of unpaired data compared to paired data, machine speech chain can be instrumental in recognizing emotions in speech where training data is limited. This study investigates the application of machine speech chain in speech emotion recognition and speech recognition of emotional speech. Our findings indicate that a model trained with 50% paired neutral emotion speech data and 22% paired non-neutral emotional speech data shows a reduction in Character Error Rate (CER) from 37.55% to 34.52% when further trained with unpaired neutral emotion speech data. The CER further decreases to 33.75% when additionally trained with combined unpaired speech data. The accuracy of recognizing non-neutral emotions ranged from 2.18% to 53.51 %, though the F1 score fluctuated, increasing by up to 20.6% and decreasing by up to 23.4%. These results suggest that the model demonstrates a bias towards the majority class, as reflected by the values of the two metrics.

关键词： Training Emotion recognition Accuracy Error analysis Natural languages Training data Speech recognition Speech enhancement Data models Speech synthesis

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Softsign Gaussian Deep Belief Neural Network (SGDBNN): A Novel Technique for Improving Pedestrian Detection Accuracy and Processing Efficiency 7

Softsign Gaussian Deep Belief Neural Network (SGDBNN): A Nov...

引用

7th International Conference on Image information Processing, ICIIP 2023

作者： Bhavani, N.P.G. Sudha, R.V. Priya, K.Shanmuga Arun Raj, S.R. Yadav, Vinay Kumar Balaram, Allam Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Electronics And Communication Engineering Tamilnadu India Knowledge Institute of Technology Department of Computer Science And Business Systems Selliampalayam Tamil Nadu Kakapalayam India KSR Institute For Engineering And Technology Department of Information Technology TamilNadu Tiruchengode637215 India U.B.D.T College of Engineering Department of Electronics & Communication Engineering Karnataka Davanagere577004 India Glocal University Glocal School of Science And Technology Uttar Pradesh India Mlr Institute of Technology Department of Computer Science And Engineering Hyderabad500043 India

ISBN: (纸本)9798350371406

Pedestrian detection is a critical task in computer vision with the aim of accurately identifying pedestrians in images or video frames. In this work, we tackle the issue of enhancing pedestrian detection precision while reducing processing time. To achieve this, we propose a novel technique called Softsign Gaussian Deep Belief Neural Network (SGDBNN) for pedestrian detection. The proposed SGDBNN technique leverages the power of recurrent deep neural networks to handle the complexities of pedestrian detection. The input layer receives images as input, which are then processed in the hidden layer. Here, a Gaussian activation function is employed to extract relevant features, effectively detecting pedestrian objects in the images. The incorporation of deep learning and recurrent behavior in the neural networks results in reduced incorrect classifications, leading to a lower false positive rate and improved pedestrian detection accuracy. The softsign activation function at the hidden layer allows the proposed technique to efficiently compare extracted features with pre-stored templates, enabling effective pedestrian identification in the images. By effectively utilizing the recurrent behavior and advanced activation functions, the SGDBNN demonstrates superior performance compared to existing methods, such as Deep Convolutional Neural Networks and Extended Deep Model. Experimental evaluations illustrate the effectiveness of the proposed SGDBNN technique. It achieves a significant increase in Positive Detection Accuracy (PDA) of up to 20% and 16%, while simultaneously reducing the False Positive Rate (FPR) by up to 18% and 3% compared to existing Deep Convolutional Neural Networks and Extended Deep Model, respectively. Additionally, the proposed SGDBNN reduces training time by up to 30% and 12% and decreases memory requirements by up to 47% and 30% when compared to existing Deep Convolutional Neural Networks and Extended Deep Model, respectively. Likewise, the SGDBNN tec

关键词： computer vision

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Revolutionizing Healthcare With Cloud Computing: The Impact of Clinical Decision Support Algorithm 1

Revolutionizing Healthcare With Cloud Computing: The Impact ...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Lenin, J. Komathi, A. Vijayan, Hima Rathinam, Anantha Raman Kasthuri, A. Srinivasan, C. Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Karnataka Bengaluru India Nadar Saraswathi College of Arts and Science College Department of Computer Science Tamilnadu Theni India S.A. Engineering College Department of Information Technology Tamil Nadu Chennai India Kl University Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350330915

The integration of cutting-edge technology, such as cloud computing and Clinical Decision Support (CDS) algorithms, is radically altering the healthcare system. This research digs into how Inference Engines, Bayesian Networks, Machine Learning Algorithms, and Natural Language Processing (NLP) have all played critical roles in reshaping the healthcare industry. medical professionals may more efficiently use CDS algorithms thanks to cloud-based technologies that streamline data storage, sharing, and processing. Decision-making is aided by Inference Engines because they provide organized insights based on preset criteria. This is where Bayesian Networks come in, with their ability to represent complicated, probabilistic interactions among variables for exact diagnoses and risk assessment. Machine learning algorithms improve predictive analysis by identifying trends in large datasets, paving the way for more individualized approaches to medical care. Chatbots and sentiment analysis are only two examples of how NLP is transforming the doctor-patient relationship by teaching computers to understand human language. Those in the medical field may greatly increase their diagnostic precision, treatment efficiency, and patient outcomes by adopting these innovations. © 2023 IEEE.

关键词： Bayesian Networks Clinical Decision Support Cloud Computing Data-driven Healthcare Healthcare Transformation Inference Engines Machine Learning Algorithms Natural Language Processing Patient-Centric Solutions Personalized Medicine

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Advancing Epilepsy Care with Neuro-Integrated Predictive Monitoring and Patient Wellbeing Using RNN Model 10

Advancing Epilepsy Care with Neuro-Integrated Predictive Mon...

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10th International Conference on Smart Computing and Communication, ICSCC 2024

作者： Mahesh, A. Sangeethalakshmi, K. Nithya, A. Shafiya Banu, M. Gnanavel, N. Muthulekshmi, M. Sri Sairam College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India R.M.K. College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India St.Joseph's Institute of Technology Department of Information Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Deapartment of Artificial Intelligence and Machine Learning Tamil Nadu Chennai India Department of Computer Sciennce and Engineering Tamil Nadu Salem India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Naduz Chennai India

ISBN: (纸本)9798350363104

This research presents a novel strategy for improving the treatment of epilepsy by combining neurological monitoring with Internet of Things (IoT) technology. The findings of this study have the potential to significantly advance the field. The methodology that has been developed allows the predictive surveillance of seizure occurrences by the seamless integration of real-time data collected from wearable devices with sophisticated analytics. The system uses algorithms designed to learn from machine data to recognize tiny physiological changes that precede seizures. This enables early intervention. The IoT-enabled platform provides therapeutic insights in addition to seizure prediction. These insights are obtained by combining information supplied by the patient with objective data. This helps to create a full knowledge of both triggers and reactions. This game-changing collaboration between neurology and the Internet of Things gives patients individualized insights, enabling them to proactively manage their illnesses and make choices based on accurate information. Ultimately, this study will alter epilepsy treatment by improving patient wellness, clinical decision-making, and the possibility of developing innovative therapeutic options. © 2024 IEEE.

关键词： Patient treatment

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Support Vector Machine for Satellite Images Classification Using Radial Basis Function Kernel Method 9th

Support Vector Machine for Satellite Images Classification U...

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9th International Conference on Computing and informatics, ICOCI 2023

作者： Mansor, Nur Suhaili Awang, Hapini Malami, Sarkin Tudu Shehu Zolkafli, Amirulikhsan Taiye, Mohammed Ahmed Maulana, Hanhan Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer Science and Information Technology Faculty of Science Sokoto State University Sokoto Sokoto852101 Nigeria Department of Cultural Sciences Faculty of Arts and Humanities Linnaeus University Vaxjo351 95 Sweden Department of Informatics Engineering and Computer Science Universitas Komputer Indonesia Bandung40134 Indonesia

ISBN: (纸本)9789819995882

Machine learning, particularly Support Vector Machines (SVM), has gained popularity in geospatial data processing and image classification. Geospatial data from various sources may contain errors, impacting image classification accuracy. Traditional pixel-based and object-based methods struggle to classify complex land cover classes accurately. Previous studies explored machine learning algorithms like Random Forests, K-Nearest Neighbors, and Neural Networks. Still, they faced challenges capturing intricate relationships within images and required substantial labeled training data, leading to computational expenses. SVM with polynomial kernels was attempted in some studies, but it suffered from potential overfitting and inefficiency for large datasets. To overcome these issues, this study employed SVM with RBF and Linear kernels to classify multispectral satellite images from the SPOT-6 Satellite Imagery dataset in Sungai Kelang, Malaysia. Previous research evaluated each kernel’s performance accuracy compared using a test dataset, utilizing open-source tools like Jupyter Notebooks and Python libraries to explore SVM’s potential as a high-performance satellite image classification technique. The findings revealed that SVM with RBF kernel outperformed SVM with polynomial or linear kernels in classifying satellite images. The RBF kernel’s robustness allowed SVM to model intricate decision boundaries and capture complex patterns in the image data, making it suitable for tasks with non-linearly separable data. The study introduces a new methodology and theoretical contribution to image classification-related literature, shedding light on the efficacy of SVM-RBF for geospatial data processing. It provides an alternative to traditional approaches for complex image classification tasks. Moreover, the research assists in selecting the optimal algorithm for remote sensing and satellite imagery applications. © 2024, The Author(s), under exclusive license to Springer Nature Si

关键词： Image classification

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Automated Health Monitoring System for Coma Patients

Automated Health Monitoring System for Coma Patients

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Latha, R. Raman, Ramakrishnan Senthil Kumar, T. J Rawandale, Chandrashekhar Meenakshi, R. Srinivasan, C. K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy India Maharashtra Pune India School of Information Technology and Engineering Vit University Tamil Nadu Vellore India Symbiosis Law School Uttar Pradesh Noida India Maharashtra Pune India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350300857

A patient in a coma is unconscious and unable to communicate. A real-time monitoring system called the IoT-Based Healthcare Monitor for Coma Patients is intended to provide coma patients with a safe and secure environment. The system continually monitors the patient's vital signs and movement in the bed using a variety of sensors, including heart rate, blood pressure, temperature, respiration, and PIR sensors. Videos or images of the patient may be taken using a camera for monitoring and assessment. The gathered data is wirelessly transferred to an embedded Raspberry Pi device, which analyses and uploads to a cloud server. The patient's data is stored on a cloud server, notifying medical personnel of unusual readings or movements. By giving healthcare providers fast access to reliable information to decide how best to treat patients, the proposed system may enhance patient outcomes. Additionally, a user-selectable interval for all process parameters is recorded online. This will be extremely helpful for further analyses and discussions of the patient's health. © 2023 IEEE.

关键词： Blood pressure

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