Digital Commons Network^™

A Comparison Of Lexical Tokenization Methods, Nathan Culmer

Williams Honors College, Honors Research Projects

The purpose of this project was to compare tokenization methods, or methods of breaking up a text into meaningful parts for use in natural language processing. The effectiveness of several commonly used tokenization methods were investigated, including morpheme tokenization, which takes into account the linguistic features of the language. In addition, I proposed and implemented a new technique to consider the capitalization pattern of a word in the tokenization process, in order to allow this process to include more natural language features. The effectiveness of these methods was compared by using them in a sentiment analysis model for various datasets, …

Autonomous Robot For Indoor Enhanced Living (Ariel), Prabhjot Kaur

Wayne State University Dissertations

The global population is aging rapidly, with the total percentage of older adults (65 years and older) projected to increase from 10\% of the total population in 2022 to 16\% by 2050, according to the World Population Prospectus 2022 issued by the United Nations. For certain parts of the world such as Europe and North America, this translates to 1 in every 4 persons is projected to be 65 years or older by 2060. This trend raises concerns about providing quality long-term care for the older population. Moreover, according to the 2021 survey by the American Association of Retired Persons …

The Analyst’S Hierarchy Of Needs: Grounded Design Principles For Tailored Intelligence Analysis Tools, Antonio E. Girona, James C. Peter, Wenyuan Wang, R. Jordan Crouser

Computer Science: Faculty Publications

Intelligence analysis involves gathering, analyzing, and interpreting vast amounts of information from diverse sources to generate accurate and timely insights. Tailored tools hold great promise in providing individualized support, enhancing efficiency, and facilitating the identification of crucial intelligence gaps and trends where traditional tools fail. The effectiveness of tailored tools depends on an analyst’s unique needs and motivations, as well as the broader context in which they operate. This paper describes a series of focus discovery exercises that revealed a distinct hierarchy of needs for intelligence analysts. This reflection on the balance between competing needs is of particular value in …

Digraph Enabled Digital Twin And Label-Encoding Machine Learning For Scada Network's Cyber Attack Analysis In Industry 5.0, Nabeel Al-Qirim, Anoud Bani-Hani, Munir Majdalawieh, Hussam Al Hamadi, Mohammad Kamrul Hasan

All Works

False-Data Injection Attack (FDIA), Remote-Tripping Command Injection (RTCI), and System Reconfiguration Attack (SRA) on SCADA (Supervisory Control and Data Acquisition) networks impact industry 5.0 enabled smart grid components such as intelligent-electronic-device (IED), circuit-breaker, network-switch, and power transmission lines. Since the SCADA-network-based cyber-attacking flow is not in digital-twin form, it is impossible to simulate the effects of the attack. Furthermore, the string nature of these affected components' data makes it challenging to incorporate into machine-learning-enabled intelligence (CTI) processes. To visualize the attacking flow of FDIA, RTCI, and SRA cyber-attacks on SCADA networks, this paper presents a novel "Digital Twin and Machine …

Enhancing Security And Performance In Vehicular Adhoc Networks: A Machine Learning Approach To Combat Adversarial Attacks, Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel, Ahmed Raheem Abdulnabi

Mesopotamian Journal of Computer Science

Integrating Machine Learning (ML) techniques into Vehicular Adhoc Networks (VANETs) provides promising features in autonomous driving and ITS applications. In this paper, DSRC data is used to evaluate the effectiveness of different ML models, including Naive Bayes, Random Forest, KNN, and Gradient Boosting, in normal and adversarial scenarios. Since the dataset is relatively imbalanced, the Synthetic Minority Over-sampling Technique (SMOTE) is employed for sampling, and defensive distillation for improving model resilience to adversarial perturbations. From the results, it is clear that models such as Gradient Boosting and Random Forest show high accuracy in both cases, thus showing the potential of …

Data Analysis Of Twitter’S Nasdaq100 Sentiments And Topics As Indicators For News Articles Retrieval: Fine-Tuning Roberta And Rag., Kagan Timur

ICT

This study investigates the combination of sentiment analysis using the VADER lexicon and semantic analysis through Latent Dirichlet Allocation (LDA) to identify real-life events, focusing on Twitter datasets. The research shows that while sentiment analysis alone may be insufficient, combining it with semantic analysis improves the process, particularly for identifying relevant news articles and understanding brand perception on social media. The study also fine-tunes the RoBERTa model for question-answering tasks, yielding significant improvements in the SQuAD evaluation metric. The exact match (EM) score rose dramatically from 2.06% to 62%, and the F1 score improved from 9.41% to 65%. A retrieval …

Responsible Natural Language Processing To Aid Employee Performance Reviews., Grace Rubinger

ICT

This research explores the use of Natural Language Processing (NLP) techniques in assessing evaluators' written appraisals during Employee Performance Reviews (EPRs), aiming to address biases inherent in traditional methods. By integrating Responsible Artificial Intelligence (AI) and foundational Large Language Models (LLMs), the study seeks to enhance the objectivity, fairness, and ethical transparency of performance evaluations. It highlights the potential of AI systems to ensure comprehensive assessments while promoting trust, ethical standards, and employee retention.

The research also aims to advance the field of AI Ethics in practical Human Resources Management (HRM) applications, particularly through NLP-driven tools. These tools are designed …

1. Navigating The New Frontier Of Generative Ai In Peer Review And Academic Writing, Chris Mayer

Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions

This chapter provides an overview of the current landscape and implications of Generative Al (GenAl) in higher education, particularly focusing on its role in academic writing and peer review. The emergence of GenAl tools such as ChatGPT is a transformative development in education, with widespread adoption at unprecedented speed. Large Language Models such as ChatGPT offer great potential for enhancing education and academic writing but also raise serious ethical concerns and tensions including access and usability, the perpetuation of Standard Academic English, and issues of linguistic justice. There is also the need for new approaches and pedagogies for how to …

Zero-Shot Cross-Lingual Pos Tagging For Filipino, Jimson Paulo Layacan, Isaiah Edri W. Flores, Katrina Bernice M. Tan, Ma. Regina Justina Estuar, Jann Railey E. Montalan, Marlene M. De Leon

Department of Information Systems & Computer Science Faculty Publications

Supervised learning approaches in NLP, exemplified by POS tagging, rely heavily on the presence of large amounts of annotated data. However, acquiring such data often requires significant amount of resources and incurs high costs. In this work, we explore zero-shot cross-lingual transfer learning to address data scarcity issues in Filipino POS tagging, particularly focusing on optimizing source language selection. Our zero-shot approach demonstrates superior performance compared to previous studies, with top-performing fine-tuned PLMs achieving F1 scores as high as 79.10%. The analysis reveals moderate correlations between cross-lingual transfer performance and specific linguistic distances–featural, inventory, and syntactic–suggesting that source languages with …

Ai-Analyst: An Ai-Assisted Sdlc Analysis Framework For Business Cost Optimization, Nuruzzaman Faruqui, Priyabrata Thatoi, Rohit Choudhary, Ivana Roncevic, Hamed Alqahtani, Iqbal H. Sarker, Shapla Khanam

Research outputs 2022 to 2026

Managing the System Development Lifecycle (SDLC) is a complex task because of its involvement in coordinating diverse activities, stakeholders, and resources while ensuring project goals are met efficiently. The complex nature of the SDLC process leaves plenty of scope for human error, which impacts the overall business cost. This paper introduces AI-Analyst, an AI-assisted framework developed using the transformer-based model with more than 150 million parameters to assist with SDLC management. It minimizes manual effort errors, optimizes resource allocation, and improves decision-making processes, resulting in substantial cost savings. The statistical analysis shows that it saves around 53.33% of costs in …

Towards Effective Developer Communication In Open Source Software Via Emotional Awareness, Mia Mohammad Imran

Theses and Dissertations

Emotions play an integral yet understudied role in open-source software development, profoundly shaping critical collaborative processes such as knowledge sharing, decision-making, and team dynamics. However, accurately detecting and analyzing emotions in developer communications poses significant challenges due to the lack of visual and auditory cues in text-based interactions. This dissertation investigates techniques to enhance the understanding and modeling of emotions within the textual artifacts of open-source projects. We conduct an extensive evaluation of existing emotion classification tools using a novel dataset of annotated GitHub comments. An error analysis reveals deficiencies in handling implicit emotional expressions and figurative language. We demonstrate …

Effort Of Reproducibility: Metrics, Methods, And Models To Navigate The Landscape Of Reproducible Research, Akhil Pandey Akella

Graduate Research Theses & Dissertations

Validating scientific claims is an elaborate process that involves researchers investing considerable time and effort to discover the validity of claims. It involves repeatability, reproducibility, and replicability, where each subsequent step requires increased effort and time. With the evolving nature of computational experiments and the exponential pace at which scientific publications are disseminated, researchers find it challenging to devote time, energy, and resources to assess claims made in papers of interest. Preemptive signals for reproducible effort can be helpful to researchers in filtering academic works of interest, thereby allowing them to adjust efforts based on need. Through this research, I …

Evaluating Computational Reproducibility Of Jupyter Notebooks Using Machine Learning And Natural Language Processing, A S M Shahadat Hossain

Graduate Research Theses & Dissertations

In recent years, computational reproducibility, which refers to achieving consistent results upon rerunning an experiment, has become one of the major concerns of various research communities. Jupyter Notebook, as a web-based computational notebook application, offers useful features for running and publishing computational experiments through interactive environments. However, rerunning notebooks does not always reproduce the experimental results. This thesis aims to develop novel methods to evaluate reproducibility by comparing different types of outputs between original and rerun notebooks. It also explores the idea of using machine learning models to predict reproducibility of notebooks automatically without the need of rerunning them. Through …

Mitigating Safety Issues In Pre-Trained Language Models: A Model-Centric Approach Leveraging Interpretation Methods, Weicheng Ma

Dartmouth College Ph.D Dissertations

Pre-trained language models (PLMs), like GPT-4, which powers ChatGPT, face various safety issues, including biased responses and a lack of alignment with users' backgrounds and expectations. These problems threaten their sociability and public application. Present strategies for addressing these safety concerns primarily involve data-driven approaches, requiring extensive human effort in data annotation and substantial training resources. Research indicates that the nature of these safety issues evolves over time, necessitating continual updates to data and model re-training—an approach that is both resource-intensive and time-consuming. This thesis introduces a novel, model-centric strategy for understanding and mitigating the safety issues of PLMs by …

การจำแนกและการประเมินประสบการณ์ผู้ใช้จากบทวิจารณ์ของผู้ใช้โมไบล์แอปพลิเคชันโดยใช้การเรียนรู้ของเครื่อง, จิราพร รามจุล

Chulalongkorn University Theses and Dissertations (Chula ETD)

การทำความเข้าใจประสบการณ์ของผู้ใช้ เป็นสิ่งสำคัญต่อความสำเร็จและการพัฒนาอย่างต่อเนื่องของโมไบล์แอปพลิเคชัน งานวิจัยนี้นำเสนอวิธีการจำแนกบทวิจารณ์ของผู้ใช้ตามลักษณะของประสบการณ์ผู้ใช้ที่มีต่อโมไบล์แอปพลิเคชัน โดยใช้ทั้งอัลกอริทึมการเรียนรู้ของเครื่อง และการเรียนรู้เชิงลึก บทวิจารณ์ของผู้ใช้ถูกจำแนกตามมิติของประสบการณ์ผู้ใช้ในด้านการปฏิบัติและประสบการณ์ผู้ใช้ในด้านความเพลิดเพลิน ซึ่งประกอบด้วยลักษณะของประสบการณ์ผู้ใช้ทั้งหมด 8 รายการ ตามที่นิยามไว้ในแบบสอบถามประสบการณ์ผู้ใช้แบบสั้น ผลลัพธ์การจำแนกบทวิจารณ์ของผู้ใช้สามารถนำไปใช้ร่วมกับการวิเคราะห์ความรู้สึกจากบทวิจารณ์ เพื่อประเมินคะแนนประสบการณ์ผู้ใช้โดยรวมของโมไบล์แอปพลิเคชันได้อย่างอัตโนมัติ จากการทดลองประเมินประสิทธิภาพของโมเดลพบว่าเบิร์ต มีประสิทธิภาพดีกว่าซัพพอร์ตเวกเตอร์แมชีน แรนดอมฟอเรสต์ และ ลอจิสติกรีเกรสชัน โดยมีค่าความเที่ยง เท่ากับ 80%, ค่าเรียกคืน เท่ากับ 74%, ค่าเอฟวัน เท่ากับ 76%, ค่าความแม่น เท่ากับ 78% และค่าเอยูซี เท่ากับ 0.94 นอกจากนี้ค่าคะแนนประสบการณ์ผู้ใช้โดยรวมของโมไบล์แอปพลิเคชันที่คำนวณได้มีค่าสหสัมพันธ์เชิงบวกในระดับปานกลางถึงสูงเป็น 0.68 กับค่าคะแนนการจัดอันดับบนแอปสโตร์ ผลการศึกษานี้ชี้ให้เห็นถึงศักยภาพของการวิเคราะห์และประเมินคะแนนประสบการณ์ผู้ใช้อย่างอัตโนมัติในการเสริมสร้างคุณภาพของโมไบล์แอปพลิเคชันและความพึงพอใจของผู้ใช้

การค้นหาค่าสัมประสิทธ์การปล่อยก๊าซเรือนกระจกบนพื้นฐานแบบจำลองภาษาขนาดใหญ่, สุชานันท์ กระบวนยุทธ

Chulalongkorn University Theses and Dissertations (Chula ETD)

งานวิจัยนี้ดำเนินการเพื่อวิจัยแบบจำลองภาษาขนาดใหญ่และเทคนิคต่างๆ ที่ช่วยเพิ่มศักยภาพของโมเดลเหล่านี้ รวมถึงวิศวกรรมที่รวดเร็วและการขยายความรู้เฉพาะ การศึกษานี้เกี่ยวข้องกับการออกแบบและพัฒนา AI agent เพื่อส่งคืนค่าสัมประสิทธ์ปล่อยก๊าซเรือนกระจกโดยอ้างอิงจากฐานข้อมูลที่ดูแลโดยองค์การบริหารก๊าซเรือนกระจก (องค์การมหาชน) ของประเทศไทย งานวิจัยนี้จะใช้ RAG เพื่อค้นหาข้อมูลที่ถูกจัดเก็บไว้ซึ่งมีความคล้ายคลึงกันมากที่สุดกับข้อมูลรับเข้า จากนั้น AI Agent จะส่งคืนค่าคำตอบเพียงคำตอบเดียวเดียวของค่าสัมประสิทธิ์การปล่อยก๊าซเรือนกระจกที่เกี่ยวข้องกับกิจกรรมที่รับเข้ามา จากการประเมินประสิทธิภาพของ LLM ขนาดกลางและขนาดใหญ่ที่ถูกเลือกทั้งห้าแบบจำลองนั้นมีแนวโน้มที่ดี

Affinity-Based Multi-Character Storyboard Generation Using Rewrite Rules, Ram Sukkirtkijpibul

Chulalongkorn University Theses and Dissertations (Chula ETD)

A game story contains interactions between characters as well as the game world. Certain genres of games such as Visual Novels and Role-Playing Games rely on their stories to attract players. There were several proposed algorithms for story generation. They focused mainly on story generation for one main character. To our knowledge, there has not been a generator with support for multiple main characters, where each main character has their own storyline that could overlap with other main characters’ stories. With multiple main characters, players can see the game world through different viewpoints, thus getting more immersed into the game's …

Improving Thai-Dialect Automatic Speech Recognition With Curriculum Learning, Artit Suwanbandit

Chulalongkorn University Theses and Dissertations (Chula ETD)

Automatic Speech Recognition (ASR) has been the foremost feature and integrated in many real-world usages for decades. The novel deep learning approaches prevail in the ASR field as a model architecture. However, ASR still struggles with low-resource data, especially in the Thai dialectal language. Transfer learning is the conventional approach in the machine learning field for escalating the performance of low-resource settings. In the scenario that additional fine-tuning tasks are not achievable, exerting all efficiency of target data is an accountability. To overcome stagnation, we proposed transfer-based curriculum learning for low-resource dialectal ASR, Phonemetically-induced subword Out-of-vocabulary rate (PhIS-OOV), and Model …

Grammatical Error Correction In Thai Sentences For Deaf Students, Supachan Traitruengsakul

Chulalongkorn University Theses and Dissertations (Chula ETD)

Deaf students encounter challenges in written communication due to errors such as insertion, deletion, disorder, misusage, and misspellings. Grammatical error correction (GEC) technology can help mitigate these issues. However, existing GEC models are primarily trained on online resources from second-language hearing learners. In contrast, sentences written by deaf students suffer from a variety of errors not typically found elsewhere. To address this issue, we create the Thai Deaf Corpus (TDC), focusing on identifying and analyzing errors among deaf students in grades 7-12 across four deaf schools. Additionally, we introduce a two-stage system for the Thai-GEC model, automatically detecting and correcting …

A Unified Framework For Contextual And Factoid Question Generation, Chenhe Dong, Ying Shen, Shiyang Lin, Zhenzhou Lin, Yang Deng

Research Collection School Of Computing and Information Systems

Question generation (QG) aims to automatically generate fluent and relevant questions, where the two most mainstream directions are generating questions from unstructured contextual texts (CQG), such as news articles, and generating questions from structured factoid texts (FQG), such as knowledge graphs or tables. Existing methods for these two tasks mainly face challenges of limited internal structural information as well as scarce background information, while these two tasks can benefit each other for alleviating these issues. For example, when meeting the entity mention “United Kingdom” in CQG, it can be inferred that it is a country in European continent based on …

Cyberbullying Text Identification: A Deep Learning And Transformer-Based Language Modeling Approach, Khalid Saifullah, Muhammad Ibrahim Khan, Suhaima Jamal, Iqbal H. Sarker

Research outputs 2022 to 2026

In the contemporary digital age, social media platforms like Facebook, Twitter, and YouTube serve as vital channels for individuals to express ideas and connect with others. Despite fostering increased connectivity, these platforms have inadvertently given rise to negative behaviors, particularly cyberbullying. While extensive research has been conducted on high-resource languages such as English, there is a notable scarcity of resources for low-resource languages like Bengali, Arabic, Tamil, etc., particularly in terms of language modeling. This study addresses this gap by developing a cyberbullying text identification system called BullyFilterNeT tailored for social media texts, considering Bengali as a test case. The …

Spatially-Aware Speaker For Vision-And-Language Navigation Instruction Generation, Muraleekrishna Gopinathan, Martin Masek, Jumana Abu-Khalaf, David Suter

Research outputs 2022 to 2026

Embodied AI aims to develop robots that can understand and execute human language instructions, as well as communicate in natural languages. On this front, we study the task of generating highly detailed navigational instructions for the embodied robots to follow. Although recent studies have demonstrated significant leaps in the generation of step-by-step instructions from sequences of images, the generated instructions lack variety in terms of their referral to objects and landmarks. Existing speaker models learn strategies to evade the evaluation metrics and obtain higher scores even for low-quality sentences. In this work, we propose SAS (Spatially-Aware Speaker), an instruction generator …

Unifying Context With Labeled Property Graph: A Pipeline-Based System For Comprehensive Text Representation In Nlp, Ali Hur, Naeem Janjua, Mohiuddin Ahmed

Research outputs 2022 to 2026

Extracting valuable insights from vast amounts of unstructured digital text presents significant challenges across diverse domains. This research addresses this challenge by proposing a novel pipeline-based system that generates domain-agnostic and task-agnostic text representations. The proposed approach leverages labeled property graphs (LPG) to encode contextual information, facilitating the integration of diverse linguistic elements into a unified representation. The proposed system enables efficient graph-based querying and manipulation by addressing the crucial aspect of comprehensive context modeling and fine-grained semantics. The effectiveness of the proposed system is demonstrated through the implementation of NLP components that operate on LPG-based representations. Additionally, the proposed …

Body-Oriented Gesture Generation System For Medical Interpreter Robots Based On Reinforcement Learning From Human Feedback, Tung Ngo, Emma Murphy, Conor Mcginn, Robert Ross

Conference papers

Medical interpreters are crucial in facilitating communication between healthcare stakeholders who speak different languages. Body-oriented gestures convey critical information essential for accurate and high-quality interpretation in healthcare settings. This study introduces a body-oriented gesture generation system designed for medical interpreter robots based on reinforcement learning from human feedback (RLHF). The system allows robots to interpret more naturally and improve over time through interactions with humans. By adopting a human-centered development approach, we tailor our system to address the actual needs of healthcare stakeholders.

Infusing Machine Learning And Computational Linguistics Into Clinical Notes, Funke V. Alabi, Onyeka Omose, Omotomilola Jegede

Mathematics & Statistics Faculty Publications

Entering free-form text notes into Electronic Health Records (EHR) systems takes a lot of time from clinicians. A large portion of this paper work is viewed as a burden, which cuts into the amount of time doctors spend with patients and increases the risk of burnout. We will see how machine learning and computational linguistics can be infused in the processing of taking clinical notes. We are presenting a new language modeling task that predicts the content of notes conditioned on historical data from a patient's medical record, such as patient demographics, lab results, medications, and previous notes, with the …

Assessing The Impact Of Chatbot-Human Personality Congruence On User Behavior: A Chatbot-Based Advising System Case, Mohammad Amin Kuhail, Mohamed Bahja, Ons Al-Shamaileh, Justin Thomas, Amina Alkazemi, Joao Negreiros

All Works

Chatbot personality has been demonstrated to influence user behavior, such as trust and intended engagement. However, previous research on chatbot-user personality congruence’s influence on user behavior is scant despite its significance in human-human conversations. This study explores the effect of chatbot-human personality trait congruence on user behavior in the context of a chatbot-based advising system. In this study, 54 college students interacted with chatbots with three different personalities (extraversion, agreeableness, and conscientiousness) and rated their trust, usage intention, and intended engagement with the chatbots. Additionally, 18 participants were interviewed to gain further insights into their perceptions and evaluations of the …

Robustsentembed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive Learning, Javad Rafiei Asl, Prajwal Panzade, Eduardo Blanco, Daniel Takabi, Zhipeng Cai

School of Cybersecurity Faculty Publications

Pre-trained language models (PLMs) have consistently demonstrated outstanding performance across a diverse spectrum of natural language processing tasks. Nevertheless, despite their success with unseen data, current PLM-based representations often exhibit poor robustness in adversarial settings. In this paper, we introduce RobustSentEmbed, a self-supervised sentence embedding framework designed to improve both generalization and robustness in diverse text representation tasks and against a diverse set of adversarial attacks. Through the generation of high-risk adversarial perturbations and their utilization in a novel objective function, RobustSentEmbed adeptly learns high-quality and robust sentence embeddings. Our experiments confirm the superiority of RobustSentEmbed over state-of-the-art representations. Specifically, …

A Chinese Power Text Classification Algorithm Based On Deep Active Learning, Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu

Computer Science Faculty Publications

The construction of knowledge graph is beneficial for grid production, electrical safety protection, fault diagnosis and traceability in an observable and controllable way. Highly-precision text classification algorithm is crucial to build a professional knowledge graph in power system. Unfortunately, there are a large number of poorly described and specialized texts in the power business system, and the amount of data containing valid labels in these texts is low. This will bring great challenges to improve the precision of text classification models. To offset the gap, we propose a classification algorithm for Chinese text in the power system based on deep …

Can Large Language Models Discern Evidence For Scientific Hypotheses? Case Studies In The Social Sciences, Sai Koneru, Jian Wu, Sarah Rajtmajer

Computer Science Faculty Publications

Hypothesis formulation and testing are central to empirical research. A strong hypothesis is a best guess based on existing evidence and informed by a comprehensive view of relevant literature. However, with exponential increase in the number of scientific articles published annually, manual aggregation and synthesis of evidence related to a given hypothesis is a challenge. Our work explores the ability of current large language models (LLMs) to discern evidence in support or refute of specific hypotheses based on the text of scientific abstracts. We share a novel dataset for the task of scientific hypothesis evidencing using community-driven annotations of studies …

Forecasting Crude Oil Price Using Multiple Factors, Hind Aldabagh, Xianrong Zheng, Mohammad Najand, Ravi Mukkamala

Computer Science Faculty Publications

In this paper, we predict crude oil price using various factors that may influence its price. The factors considered are physical market, financial, and trading market factors, including seven key factors and the dollar index. Firstly, we select the main factors that may greatly influence the prices. Then, we develop a hybrid model based on a convolutional neural network (CNN) and long short-term memory (LSTM) network to predict the prices. Lastly, we compare the CNN–LSTM model with other models, namely gradient boosting (GB), decision trees (DTs), random forests (RFs), neural networks (NNs), CNN, LSTM, and bidirectional LSTM (Bi–LSTM). The empirical …