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1. From Text to Transformation: A Comprehensive Review of Large Language Models' Versatility

Author

Kaur, Pravneet, Kashyap, Gautam Siddharth, Kumar, Ankit, Nafis, Md Tabrez, Kumar, Sandeep, and Shokeen, Vikrant

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

This groundbreaking study explores the expanse of Large Language Models (LLMs), such as Generative Pre-Trained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) across varied domains ranging from technology, finance, healthcare to education. Despite their established prowess in Natural Language Processing (NLP), these LLMs have not been systematically examined for their impact on domains such as fitness, and holistic well-being, urban planning, climate modelling as well as disaster management. This review paper, in addition to furnishing a comprehensive analysis of the vast expanse and extent of LLMs' utility in diverse domains, recognizes the research gaps and realms where the potential of LLMs is yet to be harnessed. This study uncovers innovative ways in which LLMs can leave a mark in the fields like fitness and wellbeing, urban planning, climate modelling and disaster response which could inspire future researches and applications in the said avenues.

Published
2024

4. A Comprehensive System for Smart Homes with a Minimalist Information Security Framework

Author

Jha, Chandan Kumar, Biswas, Siddhartha Sankar, Nafis, Md. Tabrez, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kaiser, M. Shamim, editor, Xie, Juanying, editor, and Rathore, Vijay Singh, editor

Published
2023
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5. Towards a Two-Tier Architecture for Privacy-Enabled Recommender Systems (PeRS)

Author

Shakil, Arif, Muhammad, Sohail, Shahab Saquib, Alam, Mohammed Talha, Ubaid, Syed, Nafis, Md Tabrez, Wang, Guojun, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Wang, Guojun, editor, Choo, Kim-Kwang Raymond, editor, Ko, Ryan K. L., editor, Xu, Yang, editor, and Crispo, Bruno, editor

Published
2022
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9. Foodborne Illness Investigation Technique Using Machine Learning

Author

Alkinj, Ahmad, Ghafir, Shabina, Alam, M. Afshar, Nafis, Md Tabrez, Bansal, Jagdish Chand, Series Editor, Deep, Kusum, Series Editor, Nagar, Atulya K., Series Editor, Jeena Jacob, I., editor, Kolandapalayam Shanmugam, Selvanayaki, editor, Piramuthu, Selwyn, editor, and Falkowski-Gilski, Przemyslaw, editor

Published
2021
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10. Enterprise IoT Core Platform

Author

D’Costa, Anil Joseph, Biswas, Siddhartha Sankar, Nafis, Md Tabrez, Alam, M. Afshar, Zafar, Sherin, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Mahapatra, Rajendra Prasad, editor, Panigrahi, B. K., editor, Kaushik, Brajesh K., editor, and Roy, Sudip, editor

Published
2021
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20. Early spotting of Parkinson's illness using machine learning techniques.

Author

Kaifi, Khushter, Rahman, Nafisur, Nafis, Md. Tabrez, and Hassan, Syed Imtiyaz

Subjects
PARKINSON'S disease, SUPPORT vector machines, K-nearest neighbor classification, SPEECH disorders, RANDOM forest algorithms
Abstract

Early Parkinson's disease (PD) diagnosis is essential for preventing the illness's development and providing patients with the opportunity to take disease-specific therapy. Speech signal processing is widely used and has gained attention recently. In this project, we carry out a comparative analysis for effective dysphonia (a speech disorder) and Parkinson's disease spotting using Machine Learning methods. To distinguish between PD patients and normal individuals. We used Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LREG), and K-Nearest Neighbor (KNN) algorithms to demonstrate a robust detection process. According to experimental findings, the KNN outperformed other ML algorithms. The results of the trial conducted at UCI contain data from 31 patients, 23 of whom were diagnosed with PD. After implementing these algorithms, we found that our accuracy rates for these algorithms are NB (86.95%), RF (85.77%), SVM (79.18%), LREG (91.56%), DT (87.16%), and KNN (97.67%). [ABSTRACT FROM AUTHOR]

Published
2024
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30. Enterprise IoT Core Platform

Author

D’Costa, Anil Joseph, primary, Biswas, Siddhartha Sankar, additional, Nafis, Md Tabrez, additional, Alam, M. Afshar, additional, and Zafar, Sherin, additional

Published
2021
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33. A Secure Clustering Technique for Unstructured and Uncertain Big Data

Author

Nafis, Md Tabrez, Biswas, Ranjit, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Saeed, Khalid, editor, Chaki, Nabendu, editor, Pati, Bibudhendu, editor, Bakshi, Sambit, editor, and Mohapatra, Durga Prasad, editor

Published
2018
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36. Predictive Modelling of Glycated Hemoglobin Levels Using Machine Learning Regressors.

Author

Hashmi, Afshan, Nafis, Md Tabrez, Naaz, Sameena, Nandan, Durgesh, and Hussain, Imran

Subjects
MACHINE learning, STANDARD deviations, HYPERGLYCEMIA, PREDICTION models, GLYCOSYLATED hemoglobin
Abstract

Diabetes is a chronic condition characterized by elevated levels of blood glucose, also known as hyperglycemia. Measurement of HbA1c is a widely used blood test that provides an essential tool for monitoring diabetic progression and assessing the effectiveness of diabetes management but this test is usually not conducted until there are some symptoms of diabetes in the patient and sometimes it goes unnoticed for a longer period resulting in the late detection of the disease. This study proposes a novel approach to HbA1c Prediction using machine learning regression algorithms on various features including Age, BMI, and hematological parameters. This study also compares the performance of ten machine learning regressors on the prediction of HbA1c level using performance metrics such as Mean square error, Root mean squared error, Mean absolute error MAE, R square, Adjusted R square, and Minimum Absolute Percentage Error. Result: Linear regression was found as the best performer with an R square and adjusted R square value of 1.00, Mean square error, Root mean squared error, Mean absolute error, and Minimum Absolute Percentage Error of 0.00. A higher HbA1c Level predicted using this method should go for actual HbA1c testing for confirmation. [ABSTRACT FROM AUTHOR]

Published
2023
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42. Enhanced bat algorithm for COVID-19 short-term forecasting using optimized LSTM

Author

Rauf, Hafiz Tayyab, Gao, Jiechao, Almadhor, Ahmad, Arif, Muhammad, and Nafis, Md Tabrez

Subjects
Gaussian inertia weight, Application of Soft Computing, Gaussian distribution, COVID-19, LSTM
Abstract

The highly infectious COVID-19 critically affected the world that has stuck millions of citizens in their homes to avoid possible spreading of the disease. Researchers in different fields are continually working to develop vaccines and prevention strategies. However, an accurate forecast of the outbreak can help control the pandemic until a vaccine is available. Several machine learning and deep learning-based approaches are available to forecast the confirmed cases, but they lack the optimized temporal component and nonlinearity. To enhance the current forecasting frameworks' capability, we proposed optimized long short-term memory networks (LSTM) to forecast COVID-19 cases and reduce mean absolute error. For the optimization of LSTM, we applied bat algorithm. Furthermore, to tackle the premature convergence and local minima problem of BA, we proposed an enhanced variant of BA. The proposed version utilized Gaussian adaptive inertia weight to control the individual velocity in the entire swarm. In addition, we substitute random walk with the Gaussian walk to observe the local search mechanism. The proposed LSTM examines the personal best solution with the swarm's local best and preserves the optimal solution by combining the Gaussian walk. To evaluate the optimized LSTM, we compared it with the non-optimal version of LSTM, recurrent neural network, gated recurrent units, and other recent state-of-the-art algorithms. The experimental results prove the superiority of the optimized LSTM over other recent algorithms by obtaining 99.52 % accuracy.

Published
2021

49. Identification of Thoracic Diseases by Exploiting Deep Neural Networks.

Author

Albahli, Saleh, Rauf, Hafiz Tayyab, Arif, Muhammad, Nafis, Md Tabrez, and Algosaibi, Abdulelah

Subjects
X-ray imaging, DEEP learning, IDENTIFICATION, DISEASES
Abstract

With the increasing demand for doctors in chest related diseases, there is a 15% performance gap every five years. If this gap is not filled with effective chest disease detection automation, the healthcare industry may face unfavorable consequences. There are only several studies that targeted X-ray images of cardiothoracic diseases. Most of the studies only targeted a single disease, which is inadequate. Although some related studies have provided an identification framework for all classes, the results are not encouraging due to a lack of data and imbalanced data issues. This research provides a significant contribution to Generative Adversarial Network (GAN) based synthetic data and four different types of deep learning-based models that provided comparable results. The models include a ResNet-152 model with image augmentation with an accuracy of 67%, a ResNet-152 model without image augmentation with an accuracy of 62%, transfer learning with Inception- V3 with an accuracy of 68%, and finally ResNet-152 model with image augmentation but targeted only six classes with an accuracy of 83%. [ABSTRACT FROM AUTHOR]

Published
2021
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50. Neutrosophy Logic and its Classification: An Overview.

Author

Muzaffar, Aiman, Nafis, Md. Tabrez, and Sohail, Shahab Saquib

Subjects
*NEUTROSOPHIC logic, *LOGIC, *EXPONENTIAL functions, *SOFT computing, *CLASSIFICATION
Abstract

Over the past few years, neutrosophy has gained an exponential growth and has attracted a good number of researchers especially those who focus on soft computing based uncertainty computation. This paper presents the various techniques in neutrosophy. The various techniques are discussed lucidly which help a naïve researcher in this field to understand the on-going researches and establish a strong base. We have summarized the previous work carried out in the field of neutrosophic logic, set, measure, and also classification techniques in neutrosophy and the relevant research work has been discussed. Further, various applications in the field of neutrosophy are elaborated. The major contributions of the existing research in neutrosophy is reviewed and presented from different perspectives. The development of newer algorithms for solving the problems of neutrosophy will provide impetus to the existing research in this field. [ABSTRACT FROM AUTHOR]

Published
2020

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