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2021 International Conference on data analytics for Business and Industry, ICDABI 2021

作者： Al Telaq, Burair Hassan Hewahi, Nabil University of Bahrain Big Data Science and Analytics College of Science Sakir Bahrain University of Bahrain College of Information Technology Department of Computer Science Sakir Bahrain

ISBN: (纸本)9781665416566

The liver is one of the most vital organs of the human body. There are various disorders of liver that need early treatment by doctors. Early diagnosis and treating the patients are significant to reduce the risk. Healthcare system can benefit from various Machine Learning (ML) models to predict diseases in early stage. The aim of this study is to predict liver disease using different ML models applied on Indian Liver Patient dataset (ILPD). The models used on this work are Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), Artificial Neural Network (ANN) and various versions of Ensemble Learning (EL). The results show that ensemble learning of KNN, RF, and SVM achieved the highest accuracy of 88%. Moreover, ensemble learning of KNN with RF obtained the highest True Positive Rate (TPR) of 99% on negative cases. In addition, this paper found that the Principal Component Analysis (PCA) technique has good impact to increase the accuracy. © 2021 IEEE.

关键词： Forecasting

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Pretrained Sentence Embedding and Semantic Sentence Similarity Language Model for Text Classification in NLP

Pretrained Sentence Embedding and Semantic Sentence Similari...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： V.Valli Mayil T.Ratha Jeyalakshmi Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Data Science & Analytics Jain University School of Sciences Banglore Karnataka

A key task in the field of Natural Language Processing (NLP) is determining semantic similarity between text sentences. Sentence pair modeling, Textual similarity and language modeling are few important tasks in NLP. Traditional machine learning algorithms require an enormous quantity of training data, but it is a timeconsuming process. Pre-trained models can be modified for a variety of downstream applications since they use methods for generically learning the characteristics of neural network topologies and language representations. Bidirectional Encoder Representations from Transformers- BERT & GPT are the popular architectures in NLP which enable to use minimal fine-tuning effort to produce effective results. In this work a fine-tuned BERT model that is suitable for semantic sentence similarity which predicts the entailment, neutral and contradictory categories of sentence pairs is presented. The fine-tuning feature promotes the training phase of model whichis widely effective across different types of semantic similarity models. The performance analysis of our system shows that the fine-tuned model reduces the number of neurons in the neural network there by reducing storage and time spent in expensive training task to create deep learning model.

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Exploring Machine Learning Classifiers for Medical datasets

Exploring Machine Learning Classifiers for Medical Datasets

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2021 International Conference on data analytics for Business and Industry, ICDABI 2021

作者： Taha, Ranyah Alshakrani, Sara Hewahi, Nabil University of Bahrain Big Data Science and Analytics College of Science Sakhir Bahrain University of Bahrain College of Information Technology Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781665416566

This research paper studies different classification algorithms with the aim of finding the best Machine Learning (ML) classifiers for medical datasets to help Clinical Decision Support Systems (CDSS). This will help physicians in making more accurate and early diagnoses. Many studies used single medical dataset to evaluate ML classifiers but its unsatisfying and doesn't reflect the capability of the classifier on different medical datasets. In this research paper, eight ML classifiers experimented on five medical datasets gathered from UCI machine learning repository. The Random Forest (RF) classifier obtained the highest accuracy average with 83.822 %, also Logistic regression (LR), Decision Tree (DT) and Gradient Boosting showed similar performance with 81.666%, 81.364%, 81.034%, while AdaBoost and Gaussian Naïve Bayes (GNB) obtained 79.504% and 78.126%, Moreover, the last two classifiers in this research obtained the lower accuracy averages are K Nearest Neighbor (KNN) with 68.208% and Artificial Neural Network (ANN) with 55.49%. © 2021 IEEE.

关键词： Decision trees

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Urban Sound Classification Using DNN, CNN LSTM a Comparative Approach

Urban Sound Classification Using DNN, CNN LSTM a Comparative...

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2021 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2021

作者： Bubashait, Mohamed Hewahi, Nabil Big Data Science and Analytics Master Program College of Science University of Bahrain Sakhier Bahrain University of Bahrain Department of Computer Science College of IT Sakhier Bahrain

ISBN: (纸本)9781665440325

Like air pollution, sound pollution has grown to be a major concern for city residents, designers, and developers. Detecting and recognizing sound types and sources in cities and suburban areas or any environment have become a necessity for the quality of life as well as security. In recent years, researchers have explored many models using Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) Neural Network, and different combinations of these techniques, which produced promising results when combined with spectrogram images, or its different variations, to classify urban sounds. This research uses the DNN model performance as a baseline to compare the CNN and LSTM models' performance for classifying urban sound using Mel scale cepstral analysis (MEL) spectrum images using an open-source library called Librosa for sound processing. Models' performance was evaluated using the UrbanSound8k dataset. The CNN model underperformed with an accuracy rate of 87.15% and f1 score of 85.63% compared to both the DNN base model and the LSTM model. In contrast, comparing the LSTM model with CNN, LSTM shows better accuracy performance on test data with 90.15%, and f1 score of 90.15%. © 2021 IEEE.

关键词： Long short-term memory

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Sentiment Analysis of Tweets during COVID-19 Pandemic Using BLSTM

Sentiment Analysis of Tweets during COVID-19 Pandemic Using ...

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2021 International Conference on data analytics for Business and Industry, ICDABI 2021

作者： Tareq, Alaa Hewahi, Nabil College of Science University of Bahrain Big Data Science and Analytics Sakhir Bahrain College of Information Technology University of Bahrain Department of Computer Science Sakhir Bahrain

ISBN: (纸本)9781665416566

social media is the new way for people to express and share their thoughts and it plays a huge role in spreading the anxiety widely during the pandemics and Twitter is one of the social media channels with high number of users and daily tweets. Clarifying and understanding what people thinks and their shared opinions during such hard times can reduce the burden on the health systems and entities and redirects the concerned entities by highlighting the areas to spread the awareness in. The aim of this study is to analyze and assess the sentiment of the Tweets shared during the COVID-19 pandemic and testing the Bidirectional Long Term Short Memory (BLTSM) of Recurrent Neural Network (RNN) in predicting the sentiment class which are Negative, Positive and Neutral. The results show slight difference between the positive and negative tweets which needs an attention to spread the awareness and hence, reduce the negativity. Furthermore, the BLSTM predicted the sentiment classes (Negative, Positive and Neutral) and obtained 86.15% accuracy rate. The high accuracy concludes that BLSTM can adapt and predict the sentiment of a text. © 2021 IEEE.

关键词： COVID-19

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Parameter Efficient Fine Tuning Llama 3.1 for Answering Arabic Legal Questions: A Case Study on Jordanian Laws

Parameter Efficient Fine Tuning Llama 3.1 for Answering Arab...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammed Fasha Bassam Hammo Bilal Sowan Husam Barham Esam Al-Nsour Business Intelligence and Data Analytics Department University of Petra Amman Jordan King Abdullah II School of Information Technology University of Jordan Amman Jordan Computer Science Department University of Zarqa Zarqa Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This study uses Jordanian law as a case study to explore the fine-tuning of the Llama-3.1 large language model for Arabic question-answering. Two versions of the model- Llama-3.1-8B-bnb-4bit and Llama-3.1-8B-Instruct-bnb-4bit-were fine-tuned using parameter-efficient fine-tuning (PEFT) with LoRA adapters and 4-bit quantized models, leveraging the Unsloth framework for accelerated and resource-efficient training. A custom dataset of 6000 legal question-answer pairs was curated from Jordanian laws and formatted into structured prompts. Performance was evaluated using the BLEU and the ROUGE metrics to compare the fine-tuned models to their respective base versions. Results demonstrated improved legal reasoning and accuracy while achieving resource efficiency through quantization and optimized fine-tuning strategies. This work underscores the potential of adapting large language models for Arabic legal domains and highlights effective techniques for fine-tuning domain-specific tasks.

关键词： Training Adaptation models Technological innovation Accuracy Text analysis Law Terminology Large language models Question answering (information retrieval) Tuning

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BitmapAligner:Bit-Parallelism String Matching with MapReduce and Hadoop

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computers, Materials & Continua 2021年第9期68卷 3931-3946页

作者： Mary Aksa Junaid Rashid Muhammad Wasif Nisar Toqeer Mahmood Hyuk-Yoon Kwon Amir Hussain Department of Computer Science COMSATS University IslamabadWah47040Pakistan Department of Computer Science AIR University IslamabadAerospace and Aviation Campus Kamra43570Pakistan Department of Computer Science National Textile UniversityFaisalabad37610Pakistan Department of Industrial Engineering and Research Center for Electrical and Information Technology Seoul National University of Science and TechnologyNowon-GuSeoul01811Korea Data Science and Cyber Analytics Research Group Edinburgh Napier UniversityEdinburghEH114DYUK

Advancements in next-generation sequencer(NGS)platforms have improved NGS sequence data production and reduced the cost involved,which has resulted in the production of a large amount of genome *** downstream analysis of multiple associated sequences has become a bottleneck for the growing genomic data due to storage and space utilization issues in the domain of *** traditional string-matching algorithms are efficient for small sized data sequences and cannot process large amounts of data for downstream *** study proposes a novel bit-parallelism algorithm called BitmapAligner to overcome the issues faced due to a large number of sequences and to improve the speed and quality of multiple sequence alignment(MSA).The input files(sequences)tested over BitmapAligner can be easily managed and organized using the Hadoop distributed file *** proposed aligner converts the test file(the whole genome sequence)into binaries of an equal length of the sequence,line by line,before the sequence alignment *** Hadoop distributed file system splits the larger files into blocks,based on a defined block size,which is 128 MB by *** can accurately process the sequence alignment using the bitmask approach on large-scale sequences after sorting the *** experimental results indicate that BitmapAligner operates in real time,with a large number of ***,BitmapAligner achieves the exact start and end positions of the pattern sequence to test the MSA application in the whole genome query *** MSA’s accuracy is verified by the bitmask indexing property of the bit-parallelism extended shifts(BXS)*** dynamic and exact approach of the BXS algorithm is implemented through the MapReduce function of Apache ***,the traditional seeds-and-extend approach faces the risk of errors while identifying the pattern sequences’***,the proposed model resolves the largescale data challen

关键词： Next-generation sequencing multiple sequence alignment MapReduce Hadoop whole-genome big data bit-parallelism

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Multi-Feature Driver Drowsiness Detection

Multi-Feature Driver Drowsiness Detection

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2021 International Conference on data analytics for Business and Industry, ICDABI 2021

作者： Abdulrahman, Hajar Taha, Ranyia Al Haj, Duaa Al-Ammal, Hesham College of Science University of Bahrain Sakhir Big Data Science and Analytics Bahrain College of Information Technology University of Bahrain Sakhir Department of Computer Science Bahrain

ISBN: (纸本)9781665416566

Driver drowsiness is one of the leading causes of deadly car accidents, and therefore has become of great interest in the field of Artificial Intelligence. In this paper, convolutional neural networks (CNNs) are used to determine the state of the driver (i.e. Drowsy or not), based on 4 observed features: open eyes, closed eyes, yawn and no yawn. The data was collected from two open source website, which was severely imbalanced between the open/closed eyes images and yawn/no yawn images, the former two classes being the majority class. Augmentation techniques were applied, and three different model structures were investigated, where the best architecture yielded an accuracy rate of 95%, and a loss of 13%. The paper discusses the success and limitations of these models and possible future work on approaching this problem. © 2021 IEEE.

关键词： Machine learning

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Exploring the Intersection of IoT and Blockchain: An Analysis of Security and Privacy Risks

Exploring the Intersection of IoT and Blockchain: An Analysi...

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International Conference on computer Communication and Informatics (ICCCI)

作者： Harishma Ashok Swathi Ganesan Nalinda Somasiri Maters in Data Analytics PSG Institute of Advanced Studies Coimbatore India Department of Computer Science York St John University London UK

Internet of things (IoT) is one of the leading technologies of transforming the internet into the next level. Security of IoT is becoming a major challenge and its measures of stopping these insecurities have been developing. Improving IoT security is important since IoT provides a great environment for systems in the industrial world and other several applications. The challenges need to be addressed so that consumer’s data can be safe. Measures available are to improve the protection of access by unauthorized persons, providing a safe way to transfer data, and ensuring synchronization of the system. Blockchain has also provided a safe way for IoT devices, each device is given the public key, IoT devices scramble and send the message dependent on the public key of the objective device so just the objective device can unscramble the message.

关键词： Privacy Strips Public key Time measurement Blockchains Safety Internet of Things

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Autonomous Multi-Robot Collaboration in Virtual Environments to Perform Tasks in Industry 4.0 2

Autonomous Multi-Robot Collaboration in Virtual Environments...

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2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022

作者： Alsamhi, Saeed Hamood Saif, A.A.F. Curry, Edward Kumar, Santosh Hawbani, Ammar University of Galway Insight Centre for Data Analytics Galway Ireland IBB Univeristy Faculty of Engineering Ibb Yemen Netstars Co Ltd Tokyo Japan IIIT-Naya Raipur Department of Computer Science and Engineering Chhattisgarh India University of Science and Technology of China School of Computer Science and Technology Hefei230027 China

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

In Industry 4.0, multi-robot efficient coordination is a critical enabler for many commercial and industrial automation activities. This paper describes how a group of robots may dynamically produce actions from a common. We focus on examining the performance of autonomous multi-robot collaboration effectively and efficiently to achieve a common task and plan for the action of future task allocation dynamically, autonomously, and in a decentralized fashion. The common goal for multi-robots here is to bring the ball from the current position to predefine the desk target. Unreal Engine (UE4) is used for designing and implementing the platform of robots. The results show significant achievement in accurate trajectories for the ball taken via multi-robot collaboration in an autonomous and decentralized fashion. As a result, multi-robot collaboration can be available for numerous applications in Industry 4.0, warehouse automation, transport, risky environments, search and rescue (SAR) teams, and disaster recovery. © 2022 IEEE.

关键词： Industry 4.0

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