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Covid-19 Knowledge Graph Generation and Enabling Analysis across Healthcare, Socioeconomic, and Environmental Dimensions 26

Covid-19 Knowledge Graph Generation and Enabling Analysis ac...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Shafkat Raihan, S.M. Pratap Deb Nath, Rudra Das, Tonmoy Chandro University of Chittagong Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350359015

There are various significant factors that influence Covid-19 pandemic, such as health, socioeconomic, and environmental aspects. Researchers and administrators require better data on measures such as confirmed cases and deaths due to Covid-19 to efficiently withhold its spread. While existing Covid-19 data resources often focus on spatio-temporal aspects, most research neglects the combined impact of multiple factors. This study addresses this gap by modeling and analyzing Covid-19 epidemiological data across dimensions like location, comorbidity, and occupation. We use data warehousing (DW) technology to model and integrate data from diverse sources in the multidimensional (MD) format. Then, we enable Online Analytical Processing (OLAP)-like analysis on DWs to derive Covid-19 related insights. Besides, to make the data interoperable, and accessible, data need to be annotated, integrated, and published semantically in accordance to FAIR principles. To accomplish this, the data is modeled as a knowledge graph (KG), where the semantic relationships among data are captured in the integration process. The KG is implemented using the Resource Description Framework (RDF) model, and semantically linked with external datasets. The generated knowledge graph is analyzed through an interactive analytical interface to observe the Covid-19 confirmed cases and deaths from thirteen aspects. The performance and quality of the knowledge graph is assessed against prominent data stores modeling Covid-19 data. © 2023 IEEE.

关键词： Big Data covid-19 Data warehousing FAIR Linked Data OLAP

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Analysis of Different Machine and Deep Learning Algorithms for Audio Classification 1

Analysis of Different Machine and Deep Learning Algorithms f...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Das, Shuvendu Nailwal, Sagar Raza, Arbaz Bhuyan, Ajatray Swagat Chandigarh University Computer Science and Engineering Dept Punjab India

ISBN: (纸本)9798350342796

Due to the numerous applications in areas including voice recognition, music genre classification, ambient sound analysis, and more, the problem of assigning semantic labels to audio signals, known as audio classification. Accurately classifying audio signals into established groups like speech, musical genres, or ambient noises is the aim. In this research we use the k-nearest neighbors (KNN) algorithm to choose the most pertinent audio features and lower the dimensionality of the audio features. Then, we train various CNN models with various activation functions (ReLU, SeLU, and ELU) and evaluate how well they perform in comparison to an LSTM model. On the test set, we assess the models' precision and insight, and we talk about the benefits and drawbacks of each model. Additionally, we demonstrate that while the LSTM model can capture the long-term dependencies and context information of the audio signals, CNN models may train discriminative features that capture the temporal and spectral characteristics of various sound classes. CNN and LSTM have achieved excellent classification accuracy and extraordinary performance in collecting significant audio information by making use of the innate temporal and spectral patterns in audio data. © 2023 IEEE.

关键词： Audio Classification Deep learning Machine learning MFCC

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Deep Learning Model for Emotion Prediction from Speech, Facial Expression and Videos 5

Deep Learning Model for Emotion Prediction from Speech, Faci...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Rajyalakshmi, Chepuri Lakshminadh, K. Reddy, M. Sathyam Dept. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665474672

The rapid development of computer vision and machine learning in recent years has led to fruitful accomplishments in a variety of tasks, including the classification of objects, the identification of actions, and the recognition of faces, among other things. Nevertheless, identifying human emotions remains one of the most difficult tasks to do. To find a solution to this issue, a significant amount of work has been put in. In order to achieve higher accuracy in this reactivity towards a variety of speeches and vocal -based methods, computer intelligence, natural language modelling systems, and other similar technologies have been used. The examination of the emotions has the potential to be useful in a number of different settings. Cooperation with human computers is one example of such a field. computers can help customers recognize emotions, make wiser decisions, and create more lifelike human-robot interactions. In recent times, there has been a lot of focus placed on the ability to forecast dynamic facial emotion expressions in videos. Therefore, this work proposes a deep convolutional neural networks (CNNs) model for emotion prediction from speech samples, facial expression images, and videos with enhanced prediction accuracy and reduced loss. In addition, the speech CNN model also utilizes mel-frequency Cepstrum coefficients (MFCC) as feature extraction from given speech samples. The proposed MFCC-CNN model resulted in superior performance than traditional models. © 2023 IEEE.

关键词： Neural network models

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Optimizing Wireless Charging Schedules in Sensor Networks: A Hybrid GSA-SA Approach for On-Demand Mobile Charging 14

Optimizing Wireless Charging Schedules in Sensor Networks: A...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Mouli, Seethamraju Sudhamsu Talagondapathi, Veeraiah Kongara, Mahesh Chowdary Mahindra University Dept of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350335095

Prior work on Wireless Sensor Networks (WSNs) has found that Sensor Node (SN) low energy capacity limits their ongoing operation. Recent discoveries in the field of Wireless Energy Transfer (WET) technology has emerged as a viable solution to sensor nodes' low energy capacity. It creates dependable Wireless Rechargeable Sensor Networks (WRSNs). Recently, there has been a lot of interest in including Wireless Mobile Chargers (WMCs) in WRSNs to refill the energy of key sensor nodes (SNs) through WET. The basic concept of WET is the use of one or more (WMCs) to recharge SNs, resulting in the development of (WRSNs). In on-demand WRSN, optimize the order in which the SNs are charged (i.e., the charging schedule). Furthermore, when creating a WMC's charging schedule, we need to consider that requesting SNs creates both space and time constraints by considering these parameters. In this paper, we propose a hybrid Gravitational Search Algorithm-Simulated Annealing (GSA-SA) based recharge schedule for replenishing the sensor nodes which can be used to optimize sensor node deployment to maximize network coverage, reduce energy usage, and increase network lifespan. Our solution includes a unique strategy for efficient fitness functions. We do extensive simulations on the proposed system to show how it outperforms novel algorithms like First Come, First Served (FCFS), Nearest Job Next with Preemption (NJNP), and Gravitational Search Algorithm (GSA). In terms of charging latency. © 2023 IEEE.

关键词： Sensor nodes

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Review on Brain Tumor Detection using Custom CNN Layers and Transfer Learning on MRI images 14

Review on Brain Tumor Detection using Custom CNN Layers and ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Saxena, Akshit Singh, Jaspreet Chandigarh University Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350335095

Early diagnosis and treatment planning are greatly aided by the early identification of brain tumors. Because of its high resolution, low radiation, and low risk of patient discomfort, magnetic resonance imaging (MRI) is frequently used to diagnose brain tumors. Brain tumor identification is only one area where recent developments in convolutional neural networks (CNNs) have shown exceptional effectiveness. This article summarizes recent progress in detecting brain tumors by utilizing MRI images and bespoke CNN layers with transfer learning. The review kicks off with a discussion of the difficulties of detecting brain cancers, such as the tumors' complexity and heterogeneity and the scarcity of available annotated data. The article proceeds to go into the foundations of CNNs and their applicability to MRI image processing. To improve detection accuracy, we incorporate custom CNN layers that are tailored to capture salient tumor-specific information. The concept of transfer learning, in which CNN models trained on large-scale datasets are repurposed for brain tumor detection, is also discussed at length in the review. Using transfer learning, we can take advantage of what we've learned about general image identification to better train models to spot brain tumors. Fine-tuning, feature extraction, and other transfer learning methods are addressed at length. Recent research using custom CNN layers and transfer learning approaches to detect brain cancers in MRI images is thoroughly analyzed in this study. Among the benefits and drawbacks discussed are the methods' adaptability to small datasets, enhanced detection accuracy, and decreased training time. Also, the significance of using metrics for measuring performance and benchmark datasets for comparing methods fairly is discussed. The analysis concludes with suggestions for future study, such as the combination of functional and diffusion tensor imaging with conventional MRI scans to better detect brain tumors. Further

关键词： Magnetic resonance imaging

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Leukemia Cancer Classification from Abnormal Cell Images Using a Deep Learning Approach: A Comprehensive Study 26

Leukemia Cancer Classification from Abnormal Cell Images Usi...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Afrin, Sadia Paul, Subrata Kumer Paul, Rakhi Rani Haque, Md. Momenul Kabir, Md Shahriar Fahim, Sultan Islam, Md. Kamrul Dept. of Computer Science and Engineering Natore6431 Bangladesh

ISBN: (纸本)9798350359015

Leukemia, a malignant blood cancer, is characterized by the uncontrolled proliferation of abnormal white blood cells in the bone marrow and blood. It is a life-threatening disease with various subtypes, making precise classification crucial for effective treatment. However, accurate classification of leukemia subtypes from abnormal cell images poses a challenging problem due to the complex and subtle variations in cell morphology. In response to this challenge, this comprehensive study employs an innovative machine learning approach, utilizing the EfficientNet B3 architecture. With this model, we achieved an impressive accuracy rate of 98.95% and a minimal loss of 0.120, demonstrating its efficacy in classifying leukemia subtypes. To conduct our experiments, we utilized the Kaggle Leukemia cancer dataset, ensuring a robust and reliable evaluation of our proposed methodology. This research contributes significantly to the development of efficient tools for early diagnosis and personalized treatment strategies for leukemia. © 2023 IEEE.

关键词： Acute Lymphoblastic Leukemia (ALL) Deep Learning EfficientNet B3 Leukemia cancer

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Diabetic Retinopathy Prediction and Analysis Using Ensemble Classifier in Deep Learning Technique

Diabetic Retinopathy Prediction and Analysis Using Ensemble ...

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Hemanth, S.V. Saravanan, A. Dept. of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9781665492942

The primary cause of vision impairment is diabetic retinopathy (DR). Diabetes can cause diabetic retinopathy, a disorder that damages the retinal blood vessels and affects the eyes. According to the analysis, 90% of sight-threatening cases may be cured if it is discovered early. We proposed an Ensemble algorithm to predict diabetes from retinal images. The primary goal of this work is to enhance the accuracy and predict the result with minimum time consumption. The proposed system is a two-phase work in which, the initial bootstrap resampling method is employed for extracting the subsamples. Next, the classified decision tree establishes a voting system for finalizing the prediction result. The performance accuracy achieved by the proposed Ensemble algorithm is compared with the logistic regression and Deep neural network. The observation shows proposed enhanced Ensemble mechanism attained 96% accuracy, which is more prominent than the other models. © 2023 IEEE.

关键词： Decision trees

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Student Performance Analysis with Ensemble Progressive Prediction 5

Student Performance Analysis with Ensemble Progressive Predi...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Althaph, B. Sreenivasu, S.V.N. Reddy, D. Venkata Dept. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665474672

It is essential to have an accurate prediction of students' future performance in order to properly carry out the necessary pedagogical interventions that are required to assure students will graduate on time and with an acceptable degree. Even though there is a wealth of research on the topic of predicting student performance when it comes to finding solutions to problems or preparing for classes by utilizing datadriven methods, the topic of predicting student performance when it comes to completing degrees (for example, college programs) is much less researched and faces new challenges, there is a large amount of diversity among students in terms of their prior experiences and the courses they choose to take. The students' developing progress should be factored into the prediction. This study proposes a unique machine learning technique for forecasting student success in degree programs. This method may handle these important issues, and it is one of the main contributions of this research. The suggested technique is distinguished primarily by its two components. To begin, a structure with two layers, a bi-layered structure, is constructed for the purpose of creating predictions based on the changing performance states of students. Then, a strategy that is driven by data and is based on latent component models and Ensemble Progressive Prediction (EPP) based matrix factorization is suggested for the purpose of determining the relevance of the course, which is essential for the construction of effective base predictors. The proposed strategy achieves better performance than benchmark methods by conducting extensive simulations using a dataset of UCLA undergraduate student data that was gathered over the course of three years. © 2023 IEEE.

关键词： Forecasting

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Slang Word Detection in the Context of User Profiling in the Social Media Platformes 4

Slang Word Detection in the Context of User Profiling in the...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Samanta, Bikram Shil, Ritika Pal, Alok Ranjan Pal, Antara College of Engg. and Management Dept. of Computer Science and Engg. Kolaghat India Atharva College of Engineering Dept. of Computer Science Engg. Mumbai India

ISBN: (纸本)9798350343670

On social media platforms, people express their views in various ways, sometimes using formal words and sometimes in informal form. The informal conversations may contain jargon words i.e. slang words which might be improper for all kinds of audience. In this work, an attempt has been made to identify slang words in a text, posted on the social media platform. The overall experiment has been carried out in 4 modules. In module-1, the suspicious words from the posted text have been identified using minimum edit distance. This experiment has been implemented by the help of a dataset of printable slang words collected from Kaggle. In module-2, the topic of discussion has been analyzed by using a knowledge based approach. This part of the experiment has been carried out by creating a newspaper archive from the online repository of the "Times of India"newspaper. But, this knowledge based strategy could not produce an appreciable accuracy at its baseline. Therefore, as a modification of this technique, in module-3, the context analysis task has been implemented combining knowledge based approach and machine learning based approach. In module-4, the module-1 and module-3 are merged to tag a suspicious word with its level of abusiveness. © 2024 IEEE.

关键词： Context Analysis Slang Word Social Media Transformer User Profiling

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Multiclass Classification of Cereals Using Artificial Neural Network 11

Multiclass Classification of Cereals Using Artificial Neural...

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11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Thapliyal, Suhani Singh, Tejaswini Nainwal, Ankita Sharma, Garima University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350326147

A diverse variety of finer features of objects may now be detected, processed, analyzed, and displayed by machine learning and imaging systems from their digital photographs in real-time thanks to recent advancements in hardware and software. The food business must keep up with population expansion by raising output and raising product standards. Cereals, which are at the bottom of the food industry's pyramid and are produced for both human and animal use, are directly or indirectly tied to the rise in food production. The cornerstone of human civilization and an integral part of daily living are cereal grains. Food, energy, and raw materials for industry have all become insatiable human needs in recent years, and agricultural production has grown significantly as a result. Also, cereal grains are an essential component of human diets and significant raw materials for both domestic and international trade. Automatic segregation of cereal grains becomes significant with time and advent of new technologies. Manual efforts not only lead to errors but are also less cost efficient. This study shows the potential of machine learning techniques for cereal recognition using computer vision, and it also discusses some problems that need to be fixed or looked into further in order to hasten the use of learning algorithms. © 2023 IEEE.

关键词： Neural networks

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