Search results for: custom dataset

Authors: Tahani Aljohani, Jialin Yu, Alexandra. I. Cristea

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The more an educational system knows about a learner, the more personalised interaction it can provide, which leads to better learning. However, asking a learner directly is potentially disruptive, and often ignored by learners. Especially in the booming realm of MOOC Massive Online Learning platforms, only a very low percentage of users disclose demographic information about themselves. Thus, in this paper, we aim to predict learners’ demographic characteristics, by proposing an approach using linguistically motivated Deep Learning Architectures for Learner Profiling, particularly targeting gender prediction on a FutureLearn MOOC platform. Additionally, we tackle here the difficult problem of predicting the gender of learners based on their comments only – which are often available across MOOCs. The most common current approaches to text classification use the Long Short-Term Memory (LSTM) model, considering sentences as sequences. However, human language also has structures. In this research, rather than considering sentences as plain sequences, we hypothesise that higher semantic - and syntactic level sentence processing based on linguistics will render a richer representation. We thus evaluate, the traditional LSTM versus other bleeding edge models, which take into account syntactic structure, such as tree-structured LSTM, Stack-augmented Parser-Interpreter Neural Network (SPINN) and the Structure-Aware Tag Augmented model (SATA). Additionally, we explore using different word-level encoding functions. We have implemented these methods on Our MOOC dataset, which is the most performant one comparing with a public dataset on sentiment analysis that is further used as a cross-examining for the models' results.

Keywords: deep learning, data mining, gender predication, MOOCs

Procedia PDF Downloads 146

Authors: Harshit Mittal, Neeraj Garg

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Hand symbol recognition is a pivotal component in the domain of computer vision, with far-reaching applications spanning sign language interpretation, human-computer interaction, and accessibility. This research paper discusses the approach with the integration of the Canny Edge algorithm and convolutional neural network. The significance of this study lies in its potential to enhance communication and accessibility for individuals with hearing impairments or those engaged in gesture-based interactions with technology. In the experiment mentioned, the data is manually collected by the authors from the webcam using Python codes, to increase the dataset augmentation, is applied to original images, which makes the model more compatible and advanced. Further, the dataset of about 6000 coloured images distributed equally in 5 classes (i.e., 1, 2, 3, 4, 5) are pre-processed first to gray images and then by the Canny Edge algorithm with threshold 1 and 2 as 150 each. After successful data building, this data is trained on the Convolutional Neural Network model, giving accuracy: 0.97834, precision: 0.97841, recall: 0.9783, and F1 score: 0.97832. For user purposes, a block of codes is built in Python to enable a window for hand symbol recognition. This research, at its core, seeks to advance the field of computer vision by providing an advanced perspective on hand sign recognition. By leveraging the capabilities of the Canny Edge algorithm and convolutional neural network, this study contributes to the ongoing efforts to create more accurate, efficient, and accessible solutions for individuals with diverse communication needs.

Keywords: hand symbol recognition, computer vision, Canny edge algorithm, convolutional neural network

Procedia PDF Downloads 60

Authors: K. N. Dinesh Babu, M. V. Gopalan, G. R. Manjunatha, R. Ramaprabha, V. Rajini

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In this paper, monitoring and control of tap changer mechanism of a transformer implementation in an intelligent electronic device (IED) is discussed. Its been a custom for decades to provide a separate panel for on load tap changer control for monitoring the tap position. However this facility cannot either record or transfer the information to remote control centers. As there is a technology shift towards the smart grid protection and control standards, the need for implementing remote control and monitoring has necessitated the implementation of this feature in numerical relays. This paper deals with the programming, settings and logic implementation which is applicable to both IEC 61850 compatible and non-compatible IEDs thereby eliminating the need for separate tap changer control equipment. The monitoring mechanism has been implemented in a 28MVA, 110 /6.9kV transformer with 16 tap position with GE make T60 IED at Ultratech cement limited Gulbarga, Karnataka and is in successful service.

Keywords: transformer protection, tap changer control, tap position monitoring, on load tap changer, intelligent electronic device (IED)

Procedia PDF Downloads 589

Authors: Subodh Chandra Shakya, Rajendra Sapkota, Aakash Tamang, Shushant Pudasaini, Sujan Adhikari, Sajjan Adhikari

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Lots of efforts have been made in order to measure the semantic similarity between the text corpora in the documents. Techniques have been evolved to measure the similarity of two documents. One such state-of-art technique in the field of Natural Language Processing (NLP) is word to vector models, which converts the words into their word-embedding and measures the similarity between the vectors. We found this to be quite useful for the task of resume ranking. So, this research paper is the implementation of the word2vec model along with other Natural Language Processing techniques in order to rank the resumes for the particular job description so as to automate the process of hiring. The research paper proposes the system and the findings that were made during the process of building the system.

Keywords: chunking, document similarity, information extraction, natural language processing, word2vec, word embedding

Procedia PDF Downloads 154

Authors: Kaustav Mukherjee

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In this paper we propose a Neural Style Transfer solution whereby we have created a Lightweight Separable Convolution Kernel Based GAN Architecture (SC-GAN) which will very useful for designing filter for Mobile Phone Cameras and also Edge Devices which will convert any image to its 2D ANIMATED COMIC STYLE Movies like HEMAN, SUPERMAN, JUNGLE-BOOK. This will help the 2D animation artist by relieving to create new characters from real life person's images without having to go for endless hours of manual labour drawing each and every pose of a cartoon. It can even be used to create scenes from real life images.This will reduce a huge amount of turn around time to make 2D animated movies and decrease cost in terms of manpower and time. In addition to that being extreme light-weight it can be used as camera filters capable of taking Comic Style Shots using mobile phone camera or edge device cameras like Raspberry Pi 4,NVIDIA Jetson NANO etc. Existing Methods like CartoonGAN with the model size close to 170 MB is too heavy weight for mobile phones and edge devices due to their scarcity in resources. Compared to the current state of the art our proposed method which has a total model size of 31 MB which clearly makes it ideal and ultra-efficient for designing of camera filters on low resource devices like mobile phones, tablets and edge devices running OS or RTOS. .Owing to use of high resolution input and usage of bigger convolution kernel size it produces richer resolution Comic-Style Pictures implementation with 6 times lesser number of parameters and with just 25 extra epoch trained on a dataset of less than 1000 which breaks the myth that all GAN need mammoth amount of data. Our network reduces the density of the Gan architecture by using Depthwise Separable Convolution which does the convolution operation on each of the RGB channels separately then we use a Point-Wise Convolution to bring back the network into required channel number using 1 by 1 kernel.This reduces the number of parameters substantially and makes it extreme light-weight and suitable for mobile phones and edge devices. The architecture mentioned in the present paper make use of Parameterised Batch Normalization Goodfellow etc al. (Deep Learning OPTIMIZATION FOR TRAINING DEEP MODELS page 320) which makes the network to use the advantage of Batch Norm for easier training while maintaining the non-linear feature capture by inducing the learnable parameters

Keywords: comic stylisation from camera image using GAN, creating 2D animated movie style custom stickers from images, depth-wise separable convolutional neural network for light-weight GAN architecture for EDGE devices, GAN architecture for 2D animated cartoonizing neural style, neural style transfer for edge, model distilation, perceptual loss

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Authors: Samy A. Khalil, U. Ali Rahoma

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The measurements of solar radiation, satellite data has been routinely utilize to estimate solar energy. However, the temporal coverage of satellite data has some limits. The reanalysis, also known as "retrospective analysis" of the atmosphere's parameters, is produce by fusing the output of NWP (Numerical Weather Prediction) models with observation data from a variety of sources, including ground, and satellite, ship, and aircraft observation. The result is a comprehensive record of the parameters affecting weather and climate. The effectiveness of reanalysis datasets (ERA-5) for North Africa was evaluate against high-quality surfaces measured using statistical analysis. Estimating the distribution of global solar radiation (GSR) over five chosen areas in North Africa through ten-years during the period time from 2011 to 2020. To investigate seasonal change in dataset performance, a seasonal statistical analysis was conduct, which showed a considerable difference in mistakes throughout the year. By altering the temporal resolution of the data used for comparison, the performance of the dataset is alter. Better performance is indicate by the data's monthly mean values, but data accuracy is degraded. Solar resource assessment and power estimation are discuses using the ERA-5 solar radiation data. The average values of mean bias error (MBE), root mean square error (RMSE) and mean absolute error (MAE) of the reanalysis data of solar radiation vary from 0.079 to 0.222, 0.055 to 0.178, and 0.0145 to 0.198 respectively during the period time in the present research. The correlation coefficient (R2) varies from 0.93 to 99% during the period time in the present research. This research's objective is to provide a reliable representation of the world's solar radiation to aid in the use of solar energy in all sectors.

Keywords: solar energy, ERA-5 analysis data, global solar radiation, North Africa

Procedia PDF Downloads 93

Authors: Brindha Senthil Kumar, Payel Chakraborty, Senthil Kumar Nachimuthu, Arindam Maitra, Prem Nath

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Gastric cancer is predominantly caused by demographic and diet factors as compared to other cancer types. The aim of the study is to predict Early Gastric Cancer (ECG) from diet and lifestyle factors using supervised machine learning algorithms. For this study, 160 healthy individual and 80 cases were selected who had been followed for 3 years (2016-2019), at Civil Hospital, Aizawl, Mizoram. A dataset containing 11 features that are core risk factors for the gastric cancer were extracted. Supervised machine algorithms: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Multilayer perceptron, and Random Forest were used to analyze the dataset using Python Jupyter Notebook Version 3. The obtained classified results had been evaluated using metrics parameters: minimum_false_positives, brier_score, accuracy, precision, recall, F1_score, and Receiver Operating Characteristics (ROC) curve. Data analysis results showed Naive Bayes - 88, 0.11; Random Forest - 83, 0.16; SVM - 77, 0.22; Logistic Regression - 75, 0.25 and Multilayer perceptron - 72, 0.27 with respect to accuracy and brier_score in percent. Naive Bayes algorithm out performs with very low false positive rates as well as brier_score and good accuracy. Naive Bayes algorithm classification results in predicting ECG showed very satisfactory results using only diet cum lifestyle factors which will be very helpful for the physicians to educate the patients and public, thereby mortality of gastric cancer can be reduced/avoided with this knowledge mining work.

Keywords: Early Gastric cancer, Machine Learning, Diet, Lifestyle Characteristics

Procedia PDF Downloads 158

Authors: Juni C. Kim, Anna R. Robuck, Douglas I. Walker

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The human exposome, which includes chemical exposures over the lifetime and their effects, is now recognized as an important measure for understanding human health; however, the complexity of the data makes the identification of environmental chemicals challenging. The goal of our project was to establish a computational workflow for the improved identification of environmental pollutants containing chlorine or bromine. Using the “pattern. search” function available in the R package NonTarget, we wrote a multifunctional script that searches mass spectral clusters from untargeted gas-chromatography high-resolution mass spectrometry (GC-HRMS) for the presence of spectra consistent with chlorine and bromine-containing organic compounds. The “pattern. search” function was incorporated into a different function that allows the evaluation of clusters containing multiple analyte fragments, has multi-core support, and provides a simplified output identifying listing compounds containing chlorine and/or bromine. The new function was able to process 46,000 spectral clusters in under 8 seconds and identified over 150 potential halogenated spectra. We next applied our function to a deidentified dataset from patients diagnosed with primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC), and healthy controls. Twenty-two spectra corresponded to potential halogenated compounds in the PSC and PBC dataset, including six significantly different in PBC patients, while four differed in PSC patients. We have developed an improved algorithm for detecting halogenated compounds in GC-HRMS data, providing a strategy for prioritizing exposures in the study of human disease.

Keywords: exposome, metabolome, computational metabolomics, high-resolution mass spectrometry, exposure, pollutants

Procedia PDF Downloads 135

Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

Procedia PDF Downloads 52

Authors: Shaukat Ali Shahee, Usha Ananthakumar

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A data set exhibits class imbalance problem when one class has very few examples compared to the other class, and this is also referred to as between class imbalance. The traditional classifiers fail to classify the minority class examples correctly due to its bias towards the majority class. Apart from between-class imbalance, imbalance within classes where classes are composed of a different number of sub-clusters with these sub-clusters containing different number of examples also deteriorates the performance of the classifier. Previously, many methods have been proposed for handling imbalanced dataset problem. These methods can be classified into four categories: data preprocessing, algorithmic based, cost-based methods and ensemble of classifier. Data preprocessing techniques have shown great potential as they attempt to improve data distribution rather than the classifier. Data preprocessing technique handles class imbalance either by increasing the minority class examples or by decreasing the majority class examples. Decreasing the majority class examples lead to loss of information and also when minority class has an absolute rarity, removing the majority class examples is generally not recommended. Existing methods available for handling class imbalance do not address both between-class imbalance and within-class imbalance simultaneously. In this paper, we propose a method that handles between class imbalance and within class imbalance simultaneously for binary classification problem. Removing between class imbalance and within class imbalance simultaneously eliminates the biases of the classifier towards bigger sub-clusters by minimizing the error domination of bigger sub-clusters in total error. The proposed method uses model-based clustering to find the presence of sub-clusters or sub-concepts in the dataset. The number of examples oversampled among the sub-clusters is determined based on the complexity of sub-clusters. The method also takes into consideration the scatter of the data in the feature space and also adaptively copes up with unseen test data using Lowner-John ellipsoid for increasing the accuracy of the classifier. In this study, neural network is being used as this is one such classifier where the total error is minimized and removing the between-class imbalance and within class imbalance simultaneously help the classifier in giving equal weight to all the sub-clusters irrespective of the classes. The proposed method is validated on 9 publicly available data sets and compared with three existing oversampling techniques that rely on the spatial location of minority class examples in the euclidean feature space. The experimental results show the proposed method to be statistically significantly superior to other methods in terms of various accuracy measures. Thus the proposed method can serve as a good alternative to handle various problem domains like credit scoring, customer churn prediction, financial distress, etc., that typically involve imbalanced data sets.

Keywords: classification, imbalanced dataset, Lowner-John ellipsoid, model based clustering, oversampling

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Authors: Felix Böck

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Nowadays, data about learners and their digital activities are collected, which could help educational institutions to better understand learning processes, improve them and be able to provide better learning assistance. In this research project, custom knowledge- and data-driven recommendation algorithms will be used to offer students in higher education integrated learning assistance. The pre-requisite for this is a learner model that is as comprehensive as possible, which should first be created and then kept up-to-date largely automatically for being able to individualize and personalize the learning experience. In order to create such a learner model, a roadmap is presented that describes the individual phases up to the creation and evaluation of the finished model. The methodological process for the research project is disclosed, and the research question of how learners can be supported in their learning with personalized, customized learning recommendations is explored.

Keywords: research agenda, user model, learner model, higher education, adaptive educational digital learning environments, personalized learning paths, recommendation system, adaptation, personalization

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Authors: Suheyla Saritas

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No matter who we are or where we live, our lives revolve around food, which is much more than a merely sustenance. As a part of the human culture, food carries complex significance and symbolic meanings. Turkish people attribute great value to food and its usage specifically tied to rites of passages of human life. Traditions, especially the ones practiced during rites of passages, such as birth, circumcisions, weddings and funerals, have always been accompanied by food in Turkish culture. Since food celebrates and symbolizes human progress in life in the culture, it also surrounds by aspects of belief, custom, magic, ritual and religion and has always been used in ceremonial context during such rites. Even though that context may be different depending on the religious, economic and social nuances of the various Turkish regions, like wheat, meat and bread, certain kinds of food play key roles during Turkish rites, generally upholding traditions. This paper highlights the sociocultural aspect of food in the rites of passages in the Turkish culture. The importance of this work also is how the ceremonial food represents the identity of Turkish people.

Keywords: food, culture, rites of passages, ritual and identity

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Authors: Anthony D. Rhodes, Manan Goel

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We provide a high fidelity deep learning algorithm (HyperSeg) for interactive video segmentation tasks using a dense convolutional network with context-aware skip connections and compressed, 'hypercolumn' image features combined with a convolutional tessellation procedure. In order to maintain high output fidelity, our model crucially processes and renders all image features in high resolution, without utilizing downsampling or pooling procedures. We maintain this consistent, high grade fidelity efficiently in our model chiefly through two means: (1) we use a statistically-principled, tensor decomposition procedure to modulate the number of hypercolumn features and (2) we render these features in their native resolution using a convolutional tessellation technique. For improved pixel-level segmentation results, we introduce a boundary loss function; for improved temporal coherence in video data, we include temporal image information in our model. Through experiments, we demonstrate the improved accuracy of our model against baseline models for interactive segmentation tasks using high resolution video data. We also introduce a benchmark video segmentation dataset, the VFX Segmentation Dataset, which contains over 27,046 high resolution video frames, including green screen and various composited scenes with corresponding, hand-crafted, pixel-level segmentations. Our work presents a improves state of the art segmentation fidelity with high resolution data and can be used across a broad range of application domains, including VFX pipelines and medical imaging disciplines.

Keywords: computer vision, object segmentation, interactive segmentation, model compression

Procedia PDF Downloads 118

Authors: S. Sudhharani

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Energy distribution networks are the main link between the energy industry and consumers and are subject to the most scrutiny and testing of any category. As a result, it is important to monitor energy levels during the distribution phase. Power distribution networks, on the other hand, remain subject to common problems, including voltage breakdown, power outages, harmonics, and capacitor switching, all of which disrupt sinusoidal waveforms and reduce the quality and power of the network. Using power appliances in the form of custom power appliances is one way to deal with energy quality issues. Dynamic Voltage Restorer (DVR), integrated with network and distribution networks, is one of these devices. At the same time, by injecting voltage into the system, it can adjust the voltage amplitude and phase in the network. In the form of injections and three-phase syncing, it is used to compensate for the difficulty of energy quality. This article examines the recent use of DVR for power compensation and provides data on the control of each DVR in distribution networks.

Keywords: dynamic voltage restorer (DVR), power quality, distribution networks, control systems(PWM)

Procedia PDF Downloads 134

Authors: Federico Cevoli

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Purinergic P2X7 receptors (P2X7R) are ligand-gated non-selective cation channels involved in several physiological and pathological processes. They are particularly promising pharmacological targets as they are present in an increasing number of different cells types. P2X7R activation is triggered following elevated concentrations of extracellular ATP, similarly to those observed in tissues injury, chronic inflammation and T-cell activation, as well as in the scrambling of phospholipids leading to membrane blebbing and apoptosis. Another hallmark of P2X7 is cell permeabilization, commonly known as “macropore” formation allowing the passage of nanometer-sized molecules up to 900Da. Recently, full-length P2X7 Cryo-EM structures revealed unique functional sites, including two cytoplasmic domains - the cytoplasmic "anchor" and "ballast". To date, the molecular units/complex by which P2X7R exerts its pathophysiological functions are unknown. Using custom-made cell-penetrating HIV-1 TAT peptides, we show for the first-time potential implications of P2X7 cytoplasmic anchor in the regulation of caspase3/7 activation as well as TNFα regulation.

Keywords: P2X7R, immunology, TAT-peptide, cell death

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Authors: Danielle Shackley, Yetunde Folajimi

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As more people turn to the internet seeking health-related information, there is more risk of finding false, inaccurate, or dangerous information. Sentiment analysis is a natural language processing technique that assigns polarity scores to text, ranging from positive, neutral, and negative. In this research, we evaluate the weight of a sentiment analysis feature added to fake health news classification models. The dataset consists of existing reliably labeled health article headlines that were supplemented with health information collected about COVID-19 from social media sources. We started with data preprocessing and tested out various vectorization methods such as Count and TFIDF vectorization. We implemented 3 Naive Bayes classifier models, including Bernoulli, Multinomial, and Complement. To test the weight of the sentiment analysis feature on the dataset, we created benchmark Naive Bayes classification models without sentiment analysis, and those same models were reproduced, and the feature was added. We evaluated using the precision and accuracy scores. The Bernoulli initial model performed with 90% precision and 75.2% accuracy, while the model supplemented with sentiment labels performed with 90.4% precision and stayed constant at 75.2% accuracy. Our results show that the addition of sentiment analysis did not improve model precision by a wide margin; while there was no evidence of improvement in accuracy, we had a 1.9% improvement margin of the precision score with the Complement model. Future expansion of this work could include replicating the experiment process and substituting the Naive Bayes for a deep learning neural network model.

Keywords: sentiment analysis, Naive Bayes model, natural language processing, topic analysis, fake health news classification model

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Authors: Yoganathan Karthik, Guhanathan Poravi

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Colorizing near-infrared (NIR) images poses unique challenges due to the absence of color information and the nuances in light absorption. In this paper, we present an approach to NIR image colorization utilizing a synthetic dataset generated from visible light images. Our method addresses two major challenges encountered in NIR image colorization: accurately colorizing objects with color variations and avoiding over/under saturation in dimly lit scenes. To tackle these challenges, we propose a Generative Adversarial Network (GAN)-based framework that learns to map NIR images to their corresponding colorized versions. The synthetic dataset ensures diverse color representations, enabling the model to effectively handle objects with varying hues and shades. Furthermore, the GAN architecture facilitates the generation of realistic colorizations while preserving the integrity of dimly lit scenes, thus mitigating issues related to over/under saturation. Experimental results on benchmark NIR image datasets demonstrate the efficacy of our approach in producing high-quality colorizations with improved color accuracy and naturalness. Quantitative evaluations and comparative studies validate the superiority of our method over existing techniques, showcasing its robustness and generalization capability across diverse NIR image scenarios. Our research not only contributes to advancing NIR image colorization but also underscores the importance of synthetic datasets and GANs in addressing domain-specific challenges in image processing tasks. The proposed framework holds promise for various applications in remote sensing, medical imaging, and surveillance where accurate color representation of NIR imagery is crucial for analysis and interpretation.

Keywords: computer vision, near-infrared images, automatic image colorization, generative adversarial networks, synthetic data

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Authors: Jinfeng Zhang, Chun-Sing Lee

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Pure nanodrugs (PNDs) – nanoparticles consisting entirely of drug molecules, have been considered as promising candidates for the next-generation nanodrugs. However, the traditional preparation method via reprecipitation faces critical challenges including low production rates, relatively large particle sizes and batch-to-batch variations. Here, for the first time, we successfully developed a novel, versatile and controllable strategy for preparing PNDs via an anodized aluminium oxide (AAO) template-assisted method. With this approach, we prepared PNDs of an anti-cancer drug (VM-26) with precisely controlled sizes reaching the sub-20 nm range. This template-assisted approach has much higher feasibility for mass production comparing to the conventional reprecipitation method and is beneficial for future clinical translation. The present method is further demonstrated to be easily applicable for a wide range of hydrophobic biomolecules without the need of custom molecular modifications and can be extended for preparing all-in-one nanostructures with different functional agents.

Keywords: drug delivery, pure nanodrugs, size control, template

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Abdul Khalique Baloch, Ali Okatan

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The quality analysis of food and vegetable from image is hot topic now a day, where researchers make them better then pervious findings through different technique and methods. In this research we have review the literature, and find gape from them, and suggest better proposed approach, design the algorithm, developed a software to measure the quality from images, where accuracy of image show better results, and compare the results with Perouse work done so for. The Application we uses an open-source dataset and python language with tensor flow lite framework. In this research we focus to sort food and vegetable from image, in the images, the application can sorts and make them grading after process the images, it could create less errors them human base sorting errors by manual grading. Digital pictures datasets were created. The collected images arranged by classes. The classification accuracy of the system was about 94%. As fruits and vegetables play main role in day-to-day life, the quality of fruits and vegetables is necessary in evaluating agricultural produce, the customer always buy good quality fruits and vegetables. This document is about quality detection of fruit and vegetables using images. Most of customers suffering due to unhealthy foods and vegetables by suppliers, so there is no proper quality measurement level followed by hotel managements. it have developed software to measure the quality of the fruits and vegetables by using images, it will tell you how is your fruits and vegetables are fresh or rotten. Some algorithms reviewed in this thesis including digital images, ResNet, VGG16, CNN and Transfer Learning grading feature extraction. This application used an open source dataset of images and language used python, and designs a framework of system.

Keywords: deep learning, computer vision, image processing, rotten fruit detection, fruits quality criteria, vegetables quality criteria

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Authors: António J. Gano, Carmen Rangel

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Currently, electric energy storage systems for stationary applications have known an increasing interest, namely with the integration of local renewable energy power sources into energy communities. Li-ion batteries are considered the leading electric storage devices to achieve this integration, and Battery Management Systems (BMS) are decisive for their control and optimum performance. In this work, the advancement of a smart BMS (sBMS) prototype with a modular distributed topology is described. The system, still under development, has a distributed architecture with modular characteristics to operate with different battery pack topologies and charge capacities, integrating adaptive algorithms for functional state real-time monitoring and management of multicellular Li-ion batteries, and is intended for application in the context of a local energy community fed by renewable energy sources. This sBMS system includes different developed hardware units: (1) Cell monitoring units (CMUs) for interfacing with each individual cell or module monitoring within the battery pack; (2) Battery monitoring and switching unit (BMU) for global battery pack monitoring, thermal control and functional operating state switching; (3) Main management and local control unit (MCU) for local sBMS’s management and control, also serving as a communications gateway to external systems and devices. This architecture is fully expandable to battery packs with a large number of cells, or modules, interconnected in series, as the several units have local data acquisition and processing capabilities, communicating over a standard CAN bus and will be able to operate almost autonomously. The CMU units are intended to be used with Li-ion cells but can be used with other cell chemistries, with output voltages within the 2.5 to 5 V range. The different unit’s characteristics and specifications are described, including the different implemented hardware solutions. The developed hardware supports both passive and active methods for charge equalization, considered fundamental functionalities for optimizing the performance and the useful lifetime of a Li-ion battery package. The functional characteristics of the different units of this sBMS system, including different process variables data acquisition using a flexible set of sensors, can support the development of custom algorithms for estimating the parameters defining the functional states of the battery pack (State-of-Charge, State-of-Health, etc.) as well as different charge equalizing strategies and algorithms. This sBMS system is intended to interface with other systems and devices using standard communication protocols, like those used by the Internet of Things. In the future, this sBMS architecture can evolve to a fully decentralized topology, with all the units using Wi-Fi protocols and integrating a mesh network, making unnecessary the MCU unit. The status of the work in progress is reported, leading to conclusions on the system already executed, considering the implemented hardware solution, not only as fully functional advanced and configurable battery management system but also as a platform for developing custom algorithms and optimizing strategies to achieve better performance of electric energy stationary storage devices.

Keywords: Li-ion battery, smart BMS, stationary electric storage, distributed BMS

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Authors: Andrei Bogdan Stanescu, Robert Stana

Abstract:

Creating private and secure communication channels between employees has become a critical aspect in order to ensure organizational integrity and avoid leaks of sensitive information. With the widespread use of modern methods of disrupting communication between users, real use-cases of advanced encryption mechanisms have emerged to avoid cyber-attackers that are willing to intercept private conversations between critical employees in an organization. This paper aims to present a custom implementation of a messaging application named “Whisper” that uses end-to-end encryption (E2EE) mechanisms and blockchain-related components to protect sensitive conversations and mitigate the risks of information breaches inside organizations. The results of this research paper aim to expand the areas of applicability of E2EE algorithms and integrations with private blockchains in chat applications as a viable method of enhancing intra-organizational communication privacy.

Keywords: end-to-end encryption, mobile communication, cryptography, communication security, data privacy

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Authors: Ravija Gunawardana, Banuka Athuraliya

Abstract:

Machine learning has emerged as a powerful tool for disease diagnosis and prediction. The use of machine learning algorithms has the potential to improve the accuracy of disease prediction, thereby enabling medical professionals to provide more effective and personalized treatments. This study focuses on developing a machine-learning model for disease prediction using symptoms and X-ray images. The importance of this study lies in its potential to assist medical professionals in accurately diagnosing diseases, thereby improving patient outcomes. Respiratory diseases are a significant cause of morbidity and mortality worldwide, and chest X-rays are commonly used in the diagnosis of these diseases. However, accurately interpreting X-ray images requires significant expertise and can be time-consuming, making it difficult to diagnose respiratory diseases in a timely manner. By incorporating machine learning algorithms, we can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The study utilized the Mask R-CNN algorithm, which is a state-of-the-art method for object detection and segmentation in images, to process chest X-ray images. The model was trained and tested on a large dataset of patient information, which included both symptom data and X-ray images. The performance of the model was evaluated using a range of metrics, including accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy rate of over 90%, indicating that it was able to accurately detect and segment regions of interest in the X-ray images. In addition to X-ray images, the study also incorporated symptoms as input data for disease prediction. The study used three different classifiers, namely Random Forest, K-Nearest Neighbor and Support Vector Machine, to predict diseases based on symptoms. These classifiers were trained and tested using the same dataset of patient information as the X-ray model. The results showed promising accuracy rates for predicting diseases using symptoms, with the ensemble learning techniques significantly improving the accuracy of disease prediction. The study's findings indicate that the use of machine learning algorithms can significantly enhance disease prediction accuracy, ultimately leading to better patient care. The model developed in this study has the potential to assist medical professionals in diagnosing respiratory diseases more accurately and efficiently. However, it is important to note that the accuracy of the model can be affected by several factors, including the quality of the X-ray images, the size of the dataset used for training, and the complexity of the disease being diagnosed. In conclusion, the study demonstrated the potential of machine learning algorithms for disease prediction using symptoms and X-ray images. The use of these algorithms can improve the accuracy of disease diagnosis, ultimately leading to better patient care. Further research is needed to validate the model's accuracy and effectiveness in a clinical setting and to expand its application to other diseases.

Keywords: K-nearest neighbor, mask R-CNN, random forest, support vector machine

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Authors: Sruthi Kannan

Abstract:

The ongoing covid pandemic has accelerated digital transformation like never before. The physical barriers brought in as a result of the pandemic are being bridged by digital alternatives. While basic collaborative activities like voice, video calling, screen sharing have been replicated in these alternatives, there are several others that require a more intimate setup. Pitching, showcasing, and providing demonstrations are an integral part of selling strategies for startups. Traditionally these have been in-person engagements, enabling a depth of understanding of the startups’ offerings. In the new normal scenario of virtual-only connects, startups are feeling the brunt of the lack of in-person connections with potential customers and investors. This poster demonstrates how a virtual reality platform has been conceptualized and custom-built for startups to engage with their stakeholders and redefine their selling strategies. This virtual reality platform is intended to provide an immersive experience for startup showcases and offers the nearest possible alternative to physical meetings for the startup ecosystem, thereby opening newer frontiers for entrepreneurial collaborations.

Keywords: collaboration, sales, startups, strategy, virtual reality

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Authors: Erin Micali, Katerina Tolstikova, Cheryl Maykel, Elizabeth Harwood

Abstract:

Female ASD is seldomly studied separately from males. Further, females with ASD are disproportionately underrepresented in the research at a rate of 3:1 (male to female). As such, much of the current understanding about female rigid repetitive behaviors (RRBs) stems from research’s understanding of male RRBs. This can be detrimental to understanding female ASD because this understanding of female RRBs largely discounts female camouflaging and the possibility that females present their autistic symptoms differently. Current literature suggests that females with ASD engage in fewer RRBs than males with ASD and when females do engage in RRBs, they are likely to engage in more subtle, less overt obsessions and repetitive behaviors than males. Method: The current study utilized a mixed methods research design to identify the type and frequency of RRBs that females with ASD engaged in by using a cross-sectional design. The researcher recruited only females to be part of the present study with the criteria they be at least age six and not have co-occurring cognitive impairment. Results: The researcher collected previous testing data (Autism Diagnostic Interview-Revised (ADI-R), Child or Adolescent/Adult Sensory Profile-2, Autism/ Empathy Quotient, Yale Brown Obsessive Compulsive Checklist, Rigid Repetitive Behavior Checklist (evaluator created list), and demographic questionnaire) from 25 total participants. The participants ages ranged from six to 52. The participants were 96% Caucasion and 4% Latin American. Qualitative analysis found the current participant pool engaged in six RRB themes including repetitive behaviors, socially restrictive behaviors, repetitive speech, difficulty with transition, obsessive behaviors, and restricted interests. The current dataset engaged in socially restrictive behaviors and restrictive interests most frequently. Within the main themes 40 subthemes were isolated, defined, and analyzed. Further, preliminary quantitative analysis was run to determine if age impacted camouflaging behaviors and overall presentation of RRBs. Within this dataset this was not founded. Further qualitative data will be run to determine if this dataset engaged in more overt or subtle RRBs to confirm or rebuff previous research. The researcher intends to run SPSS analysis to determine if there was statistical difference between each RRB theme and overall presentation. Secondly, each participant will be analyzed for presentation of RRB, age, and previous diagnoses. Conclusion: The present study aimed to assist in diagnostic clarity. This was achieved by collecting data from a female only participant pool across the lifespan. Current data aided in clarity of the type of RRBs engage in. A limited sample size was a barrier in this study.

Keywords: autism spectrum disorder, camouflaging, rigid repetitive behaviors, gender disparity

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Authors: Dzhavidan Zeinalov, Bugra Sen, Firangiz Aslanova

Abstract:

Most text-to-speech models cannot operate well in low-resource languages and require a great amount of high-quality training data to be considered good enough. Yet, with the improvements made in ASR systems, it is now much easier than ever to collect data for the design of custom text-to-speech models. In this work, our work on using the ASR model to collect data to build a viable text-to-speech system for one of the leading financial institutions of Azerbaijan will be outlined. NVIDIA’s implementation of the Tacotron 2 model was utilized along with the HiFiGAN vocoder. As for the training, the model was first trained with high-quality audio data collected from the Internet, then fine-tuned on the bank’s single speaker call center data. The results were then evaluated by 50 different listeners and got a mean opinion score of 4.17, displaying that our method is indeed viable. With this, we have successfully designed the first text-to-speech model in Azerbaijani and publicly shared 12 hours of audiobook data for everyone to use.

Keywords: Azerbaijani language, HiFiGAN, Tacotron 2, text-to-speech, transfer learning, whisper

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Authors: Guowei Hou, Lixin Yu, Wei Zhuang, Hui Qin, Xue Yang

Abstract:

Formal verification is proposed to ensure the correctness of the design and make functional verification more efficient. As cache plays a vital role in the design of System on Chip (SoC), and cache with Memory Management Unit (MMU) and cache memory unit makes the state space too large for simulation to verify, then a formal verification is presented for such system design. In the paper, a formal model checking verification flow is suggested and a new cache memory model which is called “exhaustive search model” is proposed. Instead of using large size ram to denote the whole cache memory, exhaustive search model employs just two cache blocks. For cache system contains data cache (Dcache) and instruction cache (Icache), Dcache memory model and Icache memory model are established separately using the same mechanism. At last, the novel model is employed to the verification of a cache which is module of a custom-built SoC system that has been applied in practical, and the result shows that the cache system is verified correctly using the exhaustive search model, and it makes the verification much more manageable and flexible.

Keywords: cache system, formal verification, novel model, system on chip (SoC)

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Authors: M. Naveed Akhter, Jawad Aslam, Omer Gillani

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To aid and rehabilitate increasing number of patients suffering from spinal cord injury (SCI) and stroke, a lightweight, wearable, and 3D printable exoskeletal glove has been developed. Unlike previously developed metal or fabric-based exoskeletons, this research presents the development of soft exoskeletal glove made of thermoplastic polyurethane (TPU). The drive mechanism consists of a single motor-driven antagonistic tendon to perform extension or flexion of middle and index finger. The tendon-based differential drive has been incorporated to allow for grasping of irregularly shaped objects. The design features easy 3D-printability with TPU without a need for supports. The overall weight of the glove and the actuation unit is approximately 500g. Performance of the glove was tested on a custom test-bench with integrated load cells, and the grip strength was tested to be around 30N per finger while grasping objects of irregular shape.

Keywords: 3D printable, differential drive, exoskeletal glove, rehabilitation, single motor driven

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Authors: Muneeb Ullah, Daishihan, Xiadong Young

Abstract:

Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively.

Keywords: classification, deep learning, medical images, CXR, GAN.

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Authors: Chloé Kernaléguen, Laura Kundun, Tessie Lery, Ryan Laleg, Zhangyun Tan

Abstract:

This study explores global disparities in respiratory syncytial virus (RSV) bronchiolitis incidence among children aged 0-2 years, focusing on the human development index (HDI) as a key determinant. RSV bronchiolitis poses a significant health risk to young children, influenced by factors, including socio-economic conditions captured by the HDI. Through a comprehensive systematic review and dataset selection (Switzerland, Brazil, United States of America), we formulated an HDI-SEIRS numerical model within the SEIRS framework. Results show variations in RSV bronchiolitis dynamics across countries, emphasizing the influence of HDI. Modelling reveals a correlation between higher HDI and increased bronchiolitis spread, notably in the USA and Switzerland. The ratios HDIcountry over HDImax strengthen this association, while climate disparities contribute to variations, especially in colder climates like the USA and Switzerland. The study raises the hypothesis of an indirect link between higher HDI and more frequent bronchiolitis, underlining the need for nuanced understanding. Factors like improved healthcare access, population density, mobility, and social behaviors in higher HDI countries might contribute to unexpected trends. Limitations include dataset quality and restricted RSV bronchiolitis data. Future research should encompass diverse HDI datasets to refine HDI's role in bronchiolitis dynamics. In conclusion, HDI-SEIRS models offer insights into factors influencing RSV bronchiolitis spread. While HDI is a significant indicator, its impact is indirect, necessitating a holistic approach to effective public health policies. This analysis sets the stage for further investigations into multifaceted interactions shaping bronchiolitis dynamics in diverse socio-economic contexts.

Keywords: bronchiolitis propagation, HDI influence, respiratory syncytial virus, SEIRS model

Procedia PDF Downloads 65