Other Computer Engineering Commons^™

Bibliometric Of Feature Selection Using Optimization Techniques In Healthcare Using Scopus And Web Of Science Databases, Rahul Joshi, Harita Gadikta, Saneeka Kharat, Soumi Mandal, Kalyani Kadam, Anupkumar M. Bongale Dr., Siddhant Pandit

Library Philosophy and Practice (e-journal)

Feature selection technique is an important step in the prediction and classification process, primarily in data mining related aspects or related to medical field. Feature selection is immersive with the errand of choosing a subset of applicable features that could be utilized in developing a prototype. Medical datasets are huge in size; hence some effective optimization techniques are required to produce accurate results. Optimization algorithms are a critical function in medical data mining particularly in identifying diseases since it offers excellent effectiveness in minimum computational expense and time. The classification algorithms also produce superior outcomes when an objective function is ...

Bert For Question Answering On Bioasq, Eric R. Fu, Rikel Djoko, Maysam Mansor, Robert Slater

SMU Data Science Review

Machine reading comprehension and question answering are topics of considerable focus in the field of Natural Language Processing (NLP). In recent years, language models like Bidirectional Encoder Representations from Transformers (BERT) [3] have been very successful in language related tasks like question answering. The difficulty of the question answering task lies in developing accurate representations of language and being able to produce answers for questions. In this study, the focus is to investigate how to train and fine tune a BERT model to improve its performance on BioASQ, a challenge on large scale biomedical question answering. Our most accurate BERT ...

Recent Advances And Machine Learning Techniques On Sickle Cell Disease, Noorh H. Alharbi, Rana O. Bameer, Shahad S. Geddan, Hajar M. Alharbi

Future Computing and Informatics Journal

Sickle cell disease is a severe hereditary disease caused by an abnormality of the red blood cells. The current therapeutic decision-making process applied to sickle cell disease includes monitoring a patient’s symptoms and complications and then adjusting the treatment accordingly. This process is time-consuming, which might result in serious consequences for patients’ lives and could lead to irreversible disease complications. Artificial intelligence, specifically machine learning, is a powerful technique that has been used to support medical decisions. This paper aims to review the recently developed machine learning models designed to interpret medical data regarding sickle cell disease. To propose ...

Use Of Image Processing Algorithms For Mine Originating Waste Grain Size Determination, Sebastian Iwaszenko

Journal of Sustainable Mining

The utilization of mineral wastes from the mining industry is one of most challenging phases in the raw materials life cycle. In many countries, there are piles of mineral waste materials that date back to the previous century. There is also a constant stream of accompanying mineral matter excavated during everyday mine operation. This stream of waste matter is particularly notable for deep coal mining. Grain size composition of waste mineral matter is one of most important characteristics of coal originating waste material. This paper presents the use of image analysis for the determination of grain size composition of mineral ...

The Ftc And Ai Governance: A Regulatory Proposal, Michael Spiro

Seattle Journal of Technology, Environmental & Innovation Law

No abstract provided.

Global Privacy Concerns Of Facial Recognition Big Data, Myranda Westbrook

Honors Theses

Facial recognition technology is a system of automatic acknowledgement that recognizes individuals by categorizing specific features of their facial structure to link the scanned information to stored data. Within the past few decades facial recognition technology has been implemented on a large scale to increase the security measures needed to access personal information. This has been specifically used in surveillance systems, social media platforms, and mobile device access control. The extensive use of facial recognition systems has created challenges as it relates to biometric information control and privacy concerns. This concern raises the cost and benefit analysis of an individual ...

Inverse Mapping Of Generative Adversarial Networks, Nicky Bayat

Electronic Thesis and Dissertation Repository

Generative adversarial networks (GANs) synthesize realistic samples (image, audio, video, etc.) from a random latent vector. While many studies have explored various training configurations and architectures for GANs, the problem of inverting a generative model to extract latent vectors of given input images/audio has been inadequately investigated. Although there is exactly one generated output per given random vector, the mapping from an image/audio to its recovered latent vector can have more than one solution. We train a deep residual neural network (ResNet18) architecture to recover a latent vector for a given target that can be used to generate ...

A Systematic Literature Review With Bibliometric Meta-Analysis Of Deep Learning And 3d Reconstruction Methods In Image Based Food Volume Estimation Using Scopus, Web Of Science And Ieee Database, Prachi Kadam, Nayana Petkar, Shraddha Phansalkar Dr.

Library Philosophy and Practice (e-journal)

Purpose- Estimation of food portions is necessary in image based dietary monitoring techniques. The purpose of this systematic survey is to identify peer reviewed literature in image-based food volume estimation methods in Scopus, Web of Science and IEEE database. It further analyzes bibliometric survey of image-based food volume estimation methods with 3D reconstruction and deep learning techniques.

Design/methodology/approach- Scopus, Web of Science and IEEE citation databases are used to gather the data. Using advanced keyword search and PRISMA approach, relevant papers were extracted, selected and analyzed. The bibliographic data of the articles published in the journals over the ...

Supervised Sentiment Analysis Model Of Textual Content For Images, Wrya Anwar Hayder

Passer Journal

Sentiment analysis is a domain in machine learning that tries to analyze people’s emotion, feeling, opinion and attitudes towards particular service or product. It aims to extract feelings and opinion from textual reviews; therefore, it is closely related to natural language processing (NLP). Social media has provided a huge amount of text reviews, which is practically impossible to read and analyze the emotions, attitudes and opinions that were expressed in those textual data. Sentiment analysis is a machine learning concept to classify a textual data according to reviewers’ emotion and attitudes about a service or product, which helps in ...

A Bibliometric Survey Of Smart Wearable In The Health Insurance Industry, Apeksha Shah, Swati Ahirrao, Shraddha Phansalkar, Ketan Kotecha

Library Philosophy and Practice (e-journal)

Smart wearables help real-time and remote monitoring of health data for effective diagnostic and preventive health care services. Wearable devices have the ability to track and monitor healthcare vitals such as heart rate, physical activities, BMI (Body Mass Index), blood pressure, and keeps an individual notified about the health status. Artificial Intelligence-enabled wearables show an ability to transform the health insurance sector. This would not only enable self-management of individual health but also help them focus from treatments to the preventions of health hazards. With this customer-centric approach to health care, it will enable the insurance companies to track the ...

An Approach To Counting Vehicles From Pre-Recorded Video Using Computer Algorithms, Mishuk Majumder

LSU Master's Theses

One of the fundamental sources of data for traffic analysis is vehicle counts, which can be conducted either by the traditional manual method or by automated means. Different agencies have guidelines for manual counting, but they are typically prepared for particular conditions. In the case of automated counting, different methods have been applied, but You Only Look Once (YOLO), a recently developed object detection model, presents new potential in automated vehicle counting. The first objective of this study was to formulate general guidelines for manual counting based on experience gained in the field. Another goal of this study was to ...

Optimizing The Performance Of Multi-Threaded Linear Algebra Libraries Based On Task Granularity, Shahrzad Shirzad

LSU Doctoral Dissertations

Linear algebra libraries play a very important role in many HPC applications. As larger datasets are created everyday, it also becomes crucial for the multi-threaded linear algebra libraries to utilize the compute resources properly. Moving toward exascale computing, the current programming models would not be able to fully take advantage of the advances in memory hierarchies, computer architectures, and networks. Asynchronous Many-Task(AMT) Runtime systems would be the solution to help the developers to manage the available parallelism. In this Dissertation we propose an adaptive solution to improve the performance of a linear algebra library based on a set of ...

Challenges To Adopting Hybrid Methodology: Addressing Organizational Culture And Change Control Problems In Enterprise It Infrastructure Projects, Harishankar Krishnakumar

Dissertations and Theses

IT infrastructure projects have long been an overlooked field superseded by the more popular software development silos and cross-functional project teams when it comes to enterprise Agile transformations. This paper presents a systematic literature review by leveraging a qualitative research methodology based on empirical evidence provided in contemporary scholarly research articles to explore how certain variables such as organizational culture- including team structure, leadership hierarchy, geolocation, etc. along with an organization’s change management processes affect the adoption of a Hybrid/Agile project management methodology, focusing on reported challenges and critical success factors that define such large-scale enterprise transformations. The ...

Bibliometric Survey On Biometric Iris Liveness Detection, Smita Khade, Dr.Swati Ahirrao, Dr. Sudeep Thepade

Library Philosophy and Practice (e-journal)

Authentication is an essential step for giving access to resources to authorized individuals and prevent leakage of confidential information. The traditional authentication systems like a pin, card, a password could not differentiate among the authorized users and fakers who have an illegal access to the system. Traditional authentication technique never alerts about the unwanted access to the system. The device that allows the automatic identification of an individual is known as a biometric system. It is not required to remember a password, card, and pin code in the Bio-metric system. Numerous biometric characteristics like the fingerprint, iris, palm print, face ...

A Bibliometric Analysis Of Online Extremism Detection, Mayur Gaikwad, Swati Ahirrao, Shraddha Pankaj Phansalkar, Ketan Kotecha

Library Philosophy and Practice (e-journal)

The Internet has become an essential part of modern communication. People are sharing ideas, thoughts, and beliefs easily, using social media. This sharing of ideas has raised a big problem like the spread of the radicalized extremist ideas. The various extremist organizations use the social media as a propaganda tool. The extremist organizations actively radicalize and recruit youths by sharing inciting material on social media. Extremist organizations use social media to influence people to carry out lone-wolf attacks. Social media platforms employ various strategies to identify and remove the extremist content. But due to the sheer amount of data and ...

A Bibliometric Survey On Cognitive Document Processing, Dipali Baviskar, Swati Ahirrao, Ketan Kotecha

Library Philosophy and Practice (e-journal)

Heterogenous and voluminous unstructured data is produced from various sources like emails, social media tweets, reviews, videos, audio, images, PDFs, scanned documents, etc. Organizations need to store this wide range of unstructured data for more and longer periods so that they can examine information all the more profoundly to make a better decision and extracting useful insights. Manual processing of such unstructured data is always a challenging, time-consuming, and expensive task for any organization. Automating unstructured document processing using Optical Character Recognition (OCR) and Robotics Process Automation (RPA), seems to have limitations, as those techniques are driven by rules or ...

A Bibliometric Survey On The Reliable Software Delivery Using Predictive Analysis, Jalaj Pachouly, Swati Ahirrao, Ketan Kotecha

Library Philosophy and Practice (e-journal)

Delivering a reliable software product is a fairly complex process, which involves proper coordination from the various teams in planning, execution, and testing for delivering software. Most of the development time and the software budget's cost is getting spent finding and fixing bugs. Rework and side effect costs are mostly not visible in the planned estimates, caused by inherent bugs in the modified code, which impact the software delivery timeline and increase the cost. Artificial intelligence advancements can predict the probable defects with classification based on the software code changes, helping the software development team make rational decisions. Optimizing ...

A Novel Energy-Efficient Sensor Cloud Model Using Data Prediction And Forecasting Techniques, Kalyan Das, Satyabrata Das, Aurobindo Mohapatra

Karbala International Journal of Modern Science

An energy-efficient sensor cloud model is proposed based on the combination of prediction and forecasting methods. The prediction using Artificial Neural Network (ANN) with single activation function and forecasting using Autoregressive Integrated Moving Average (ARIMA) models use to reduce the communication of data. The requests of the users generate in every second. These requests must be transferred to the wireless sensor network (WSN) through the cloud system in the traditional model, which consumes extra energy. In our approach, instead of one second, the sensors generally communicate with the cloud every 24 hours, and most of the requests reply using the ...

A Novel Framework Using Neutrosophy For Integrated Speech And Text Sentiment Analysis, Florentin Smarandache, Kritika Mishra, Ilanthenral Kandasamy, Vasantha Kandasamy W.B.

Mathematics and Statistics Faculty and Staff Publications

With increasing data on the Internet, it is becoming difficult to analyze every bit and make sure it can be used efficiently for all the businesses. One useful technique using Natural Language Processing (NLP) is sentiment analysis. Various algorithms can be used to classify textual data based on various scales ranging from just positive-negative, positive-neutral-negative to a wide spectrum of emotions. While a lot of work has been done on text, only a lesser amount of research has been done on audio datasets. An audio file contains more features that can be extracted from its amplitude and frequency than a ...

Forecasting Vegetation Health In The Mena Region By Predicting Vegetation Indicators With Machine Learning Models, Sachi Perera, Wenzhao Li, Erik Linstead, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the ...