Natural Language Processing - Science topic

How do reading comprehension tests (including texts and questions) generated with a controlled level of difficulty change the results of testing? In particular, compared to tests generated without taking into account the difficulty?

If you have conducted research on this topic, please share your results.

Are there any researches related to the following research questions?

1. Are large language models (LLMs) capable of generating texts and tasks for a reading test with specified psychometric properties, and also adhere to a specified taxonomy when automatically generating tasks?

2. Is it possible to use LLMs to define the difficulty and task taxonomy of a future reading literacy test based on a theoretically developed test?

3. Are LLMs capable of generating texts and tasks comparable in quality to materials developed by experts?

Please, share any results

2025 5th International Conference on Big Data, Artificial Intelligence and Risk Management (ICBAR 2025) will be held in Dongguan, China on December 12-14, 2025.

𝗖𝗼𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗪𝗲𝗯𝘀𝗶𝘁𝗲: https://ais.cn/u/iuaEje

The topics of interest for submission include, but are not limited to:

◕Track 1: Core Data Technologies

Data analysis

Data mining

Data integration and visualization

Data management, analysis, and information retrieval

Distributed data processing and integration

Open knowledge computing

Scientific research based on big data

◕Track 2: Artificial Intelligence and Algorithms

Natural language processing (NLP)

Object detection and recognition

Artificial intelligence algorithms

Distributed artificial intelligence

Risk analysis and management based on artificial intelligence technology

Network security (cybersecurity applications of AI)

Reliability engineering (AI-driven systems)

Risk management systems

Risk recognition

Big data-driven risk analysis and management

Risk governance and big data

Risk and emergency governance system

Decision management

Big data-driven management and decision-making

--- 𝗞𝗲𝘆𝗻𝗼𝘁𝗲 𝗦𝗽𝗲𝗮𝗸𝗲𝗿𝘀---

---𝗣𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻---

All papers, both invited and contributed, will be reviewed by two or three expert reviewers from the conference committees. After a careful reviewing process, all accepted papers of ICBAR 2025 conference proceedings will be published and submitted to EI Compendex, Scopus for indexing.

---𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗗𝗮𝘁𝗲𝘀---

Full Paper Submission Date: November 12, 2025

Registration Date: November 28, 2025

Final Paper Submission Date: November 28, 2025

Conference Dates: December 12-14, 2025

--- 𝗣𝗮𝗽𝗲𝗿 𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻---

Please send the full paper(word+pdf) to Submission System:

The 5th International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2025) will be held in Nanchang, China from November 28-30, 2025.

The topics of interest for submission include, but are not limited to:

◕Algorithms

▪Analysis of algorithms

▪Approximation algorithm

▪Computability theory

▪Evolutionary algorithm

▪Genetic algorithm

▪Numerical analysis

▪Online algorithm

▪Quantum algorithm

▪Randomized algorithm

▪Sorting algorithm

......

◕Artificial Intelligence

▪Natural language processing

▪Knowledge expression

▪Intelligent search

▪Machine learning

▪Perception problems

▪Pattern recognition

▪Soft computing in logic programming

▪Imprecise and uncertain management

▪Artificial life

▪Neural network

......

◕High Performance Computing

▪Network computing technology

▪Development of high-performance computing software and tools

▪Computer system evaluation technology

▪Cloud computing system

▪Mobile computing system

▪Point to point calculation

▪Grid and cluster computing Web

▪Web services and Internet Computing

▪Utility calculation

▪High performance science and Engineering Computing

▪Parallel and distributed system architecture

......

◕Image Processing

▪Image Recognition

▪Image Detection Network

▪Robot Vision

▪Clustering

▪Image Digitization

▪Image Enhancement and Restoration

▪Image Data Coding

▪Image Segmentation

▪Analog Image Processing

▪Digital Image Processing

......

Submitted paper will be peer reviewed by conference committees, and accepted papers after registration and presentation will be published in the Conference Proceedings, which will be submitted for indexing by Ei Compendex, Scopus.

Full Paper Submission Date: October 31, 2025

Registration Deadline: November 4, 2025

Final Paper Submission Date: November 14, 2025

Conference Dates: November 28-30, 2025

Please send the full paper(word+pdf) to Submission System:

Dear Researchers,

Before, knowledge was rare. People feared sharing it, but it had value. Today, knowledge is open to everyone. Yet it often lacks value because there are too much information and too little understanding.

Modern deep learning models, particularly transformer-based architectures like BERT, GPT, and their variants, have achieved state-of-the-art performance across a wide range of natural language processing (NLP) tasks. However, these models are typically static in nature, meaning they use the same computational pathway and resources for all inputs, regardless of complexity. This leads to several inefficiencies:

How can we design dynamic neural architectures that adapt their size, complexity, and computational pathways based on:

The goal is to create models that are efficient, scalable, and adaptive, without sacrificing performance on downstream tasks.

Asked in October 2025.

In the post-training of LLMs, the superiority of Reinforcement Learning (RL) over Supervised Fine-Tuning (SFT) is not primarily due to the quality of the reward model, but rather to the RL process.

(Explore how NLP tools are influencing pragmatics, stylistics, or corpus studies.

Natural Language Processing (NLP) has been around for decades, but Large Language Models (LLMs) are taking AI to the next level 🚀.

In this video, I break down: ✅ Key differences between NLP & LLM ✅ Real-world applications ✅ The future of AI in language understanding

Watch here 👉 https://youtu.be/WeTsxluHoxU

#ArtificialIntelligence #NLP #LLM #MachineLearning #FutureOfAI

I’m conducting a meta-analysis based on observational studies (over 2,000 eligible articles), and I’m exploring the use of AI or machine learning tools to assist with data extraction, given the complexity and volume of the data.

Unlike RCTs, these studies use a wide range of statistical analyses (e.g., different regression models), and variables are reported inconsistently. For example:

Given these challenges, I’m looking for AI/ML tools that can support:

🔍 Have you used any AI or NLP-based tools for complex data extraction in large-scale systematic reviews? I’d appreciate any recommendations, workflows, or lessons learned — particularly from those working with non-interventional studies.

Thanks in advance for your insights!

We are an active Arabic Natural Language processing (NLP) and AI research group doing research in Deep learning, machine learning and social network analysis for Arabic NLP.

We are looking for an RA that can work remotely on a number of NLP/Deep learning/Machine Learning projects, where can we find such candidates?

Data cleaning, analysis and visualization using various approaches.

Ability to conduct literature review and summarize them in a coherent way.

Ability to implement different ML/DL approaches using different datasets to serve specific NLP problems.

Ability to fine tuning BERT/AraBert and its different variations to serve specific NLP tasks.

Ability to communicate the experiments and results in clear English language.

Master/PhD in computer science.

Experience in Python (including numPy, sciPy, pandas, matplotlib)

Excellent working knowledge of Deep learning/Machine Learning.

Experience with word embeddings, BERT, etc.

Ability to clearly communicate technical ideas in English.

Motivated, Independent, self-learner and ability to work with diverse team.

Excellent verbal and written communication skills are required.

LLMs = large language models

AI is rapidly transforming healthcare, especially in medical documentation. Tools powered by natural language processing can now transcribe clinical conversations, auto-fill EHRs, and enhance the accuracy of patient records—reducing physician burnout and improving efficiency.

However, innovation must align with strict HIPAA compliance. AI systems that handle protected health information (PHI) must ensure:

How can the integration of Natural Language Processing (NLP) technologies support and advance second language acquisition pedagogies?

Explore how artificial intelligence is transforming medical documentation by enabling faster, more accurate, and secure workflows. This session delves into the development of AI-powered tools designed specifically for healthcare environments, with a focus on ensuring HIPAA compliance. Learn how cutting-edge technologies such as natural language processing (NLP), voice recognition, and predictive analytics are being integrated into digital health solutions to reduce clinician burnout, improve patient outcomes, and streamline administrative tasks—without compromising data privacy and regulatory standards. Whether you're a developer, clinician, or health IT leader, discover best practices and real-world strategies for building next-generation documentation systems that are as smart as they are secure.

Providing background information for those unfamiliar with the UG theory. Making the debate relevant to modern AI advancements. Encouraging multidisciplinary contributions (linguistics, NLP, cognitive science, etc.). Adding specific questions to guide responses.

To develop, test, and apply AI-driven solutions that enhance educational outcomes, personalize learning experiences, and redefine the future of teaching and school operations in alignment with UAE national goals.

In the world of Natural Language Processing, understanding morphemes—the smallest units of meaning—is key to building smarter language models and better AI.

In this quick explainer video, we break down:

https://youtu.be/EoTn-uV4oHY ✔️ Roots – the core meanings ✔️ Prefixes – modifiers that come before ✔️ Suffixes – endings that tweak meaning or form

Whether you're a student, developer, or NLP enthusiast, this is foundational knowledge that can sharpen your understanding of how machines process human language.

🎥 Watch now: https://youtu.be/EoTn-uV4oHY

#NLP #MachineLearning #AI #Linguistics #NaturalLanguageProcessing #EdTech #DeepLearning #Morphemes #LanguageTechnology #ComputationalLinguistics

(Note that this is a promotional offer which applies to all papers submitted before 31 December 2024)

Journal of Artificial Intelligence for Medical Sciences (JAIMS, Online ISSN 2666-1470) is an international peer reviewed journal that covers all aspects of theoretical, methodological and applied artificial intelligence (AI) for medical sciences, healthcare and life sciences.

The Editors welcome original research articles, comprehensive reviews, correspondences and perspectives that provide novel insights into diagnostics, drug development, care processes, treatment personalization with the support of machine/deep learning, data science, natural language processing (NLP), etc.

Read freely available Journal of Artificial Intelligence for Medical Sciences Editor in Chief: Prof. Zhisheng Huang (Vrije Universiteit Amsterdam (VU), Amsterdam, The Netherlands) Deputy Editors-in-Chief: Prof. Xue Li (The University of Queensland, Brisbane, Australia) Prof. Xingpeng Jiang (Central China Normal University, Wuhan, China) Submit your paper if you would like to be considered for this journal:

We are looking forward to receiving your work! Contact us at jaims@editorialoffice.cn

Regular Expressions in NLP Explained! | Power of Patterns

Understanding how text is processed in Natural Language Processing (NLP) often begins with mastering one powerful tool — Regular Expressions (Regex).

🔍 Want to extract specific patterns from text?

🔡 Clean and preprocess large volumes of unstructured data?

💡 Detect trends, emails, hashtags, or even sarcasm?

Regex is the unsung hero behind it all. Whether you're new to NLP or brushing up on text processing techniques, this video breaks it down in a simple, clear, and practical way.

🎥 Watch here:

✅ Perfect for:

NLP & ML beginners

Data Scientists

Python Developers

Anyone curious about the power of pattern matching

Let’s decode the language of text with the power of patterns!

#NLP #Regex #DataScience #MachineLearning #TextProcessing #Python #RegularExpressions #AI #PatternMatching #CleanData #NaturalLanguageProcessing

Understanding language starts with understanding syntax — the structure that gives meaning to words and phrases. In my latest video, we dive deep into some of the most foundational NLP concepts:

✅ Regular Expressions (Regex) in Python ✅ Morphological Analysis ✅ Parse Trees & Syntactic Structure ✅ Python Libraries for Parsing (like NLTK & spaCy)

Whether you're a machine learning engineer, linguist, or just exploring natural language processing, this tutorial will equip you with essential tools to parse and analyze language effectively.

🎥 Watch now: https://youtu.be/25rZh-VkxMw

Let me know your thoughts in the comments, and feel free to share with others learning NLP! #NLP #MachineLearning #DataScience #Python #Regex #ParseTrees #NaturalLanguageProcessing #AI #DeepLearning #NLPwithPython

Hi everyone,

I’m currently working on a project involving the integration of a locally deployed large language model (we are currently using 14b DeepSeek) with a local knowledge base. The knowledge base mainly consists of a terminology glossary (we are still discussing the best data structure for storing it) and literature documents (such as PDFs or structured text).

I’d like to ask the community:

I'm particularly interested in strategies for enabling the LLM to accurately retrieve and utilize information from the local knowledge base during inference.

I’ll be updating this post with any progress or insights we gain along the way, and I really appreciate any guidance or suggestions you may have!

Thanks in advance!

Natural Language Processing (NLP) is the magic that powers your virtual assistants, chatbots, and even autocorrect! But how exactly does it work?

In this video, I break down NLP in the simplest way possible — with real-world examples to help you understand how machines process and understand human language.

🎥 Watch here 👉 https://youtu.be/pluPbsiw5Pc

Whether you're just getting started in AI/ML or want a refresher on NLP fundamentals, this video is for you!

💬 I’d love to hear your thoughts. What NLP application excites you the most?

#NLP #AI #MachineLearning #ArtificialIntelligence #NaturalLanguageProcessing #TechExplained #EdTech #YouTubeLearning

Watch Here - https://youtu.be/pluPbsiw5Pc

会议征稿：2025人工智能、人机交互与自然语言处理国际学术会议(ICAHN 2025)

Call for papers：2025 International Conference on Natural Language Processing, Human-Computer Interaction, and Artificial Intelligence will be held in Ningbo, China, on 23rd May, 2025.

大会官网(投稿网址）：https://ais.cn/u/Zr2M32

大会时间：2025年5月23-25日

大会地点：中国-宁波

提交检索：IEEE Xplore，EI Compendex ,Scopus

随着信息技术的快速发展，数据产生的规模和多样性急剧增加，尤其是非结构化数据（如文本、语音等）的增长，推动了自然语言处理（NLP）和人机交互领域的变革。2025自然语言处理、人机交互与人工智能国际学术会议将于2025年5月23日在中国宁波举办，本会议旨在汇聚自然语言处理、人机交互与人工智能领域的研究者、学者和工程师，共同探讨当前技术前沿、应用趋势与未来发展的方向。通过学术交流与合作，本会议希望能够促进跨领域的研究合作、分享最新的研究成果，同时为与会者提供一个展示其研究工作的良好平台。为保证本次会议的学术质量，吸引更多的原创高水平学术论文，现公开征稿，欢迎广大从事有关自然语言处理和信息处理的教学、科研人员和学生踊跃投稿。

1、人工智能：人工智能应用与技术、人工神经网络、人工智能工具与应用、机器学习、机器人、多智能体系统等

2、人机交互：光学手势识别系统、生物识别、眼动追踪和表情识别、光势行为分析、传感器技术、光学传感与虚拟现实交互、光学跟踪与定位系统、声光传感融合系统、基于VR的交互式教学等

3、自然语言处理：语音识别系统优化、语音合成、情感分析、语言模型、机器翻译、对话系统、人机交互、语义理解和文本分析等

ICAHN 2025所有的投稿都必须经过2-3位组委会专家审稿，经过严格的审稿之后，会议所录用论文将以论文集的形式提交IEEE CPS出版社出版，出版后提交 EI Compendex, Scopus检索。

*开放给所有投稿作者与自费参会人员；

*针对论文或者论文里面的研究做一个10-15min的英文汇报，需要自备PPT，无固定模板要求，汇报和PPT要求英文，建议在PPT首页放上会议简称和logo，PPT格式建议16:9。

*开放给所有投稿作者与自费参会人员；

*格式：全英-A1尺寸-竖版，需自制；制作后提交海报图片至会议邮箱aihinlp@163.com，主题及海报命名格式为：现场参会/线上参会-姓名-论文订单号。

*仅开放给自费参会人员，（3人及以上）组队参会优惠请联系会议秘书。

Hello everyone,

I am currently seeking research collaborations in Artificial Intelligence as part of a pre-doctoral project. My goal is to actively prepare for a future PhD by gaining hands-on experience in applied research.

My skills include:

• Strong foundation in artificial intelligence, machine learning, and deep learning.

• Proficiency in frameworks such as TensorFlow, PyTorch, and scikit-learn.

• Experience in software development using Python, C, C++, Go, Java, Node.js and other relevant technologies.

Interest in topics such as:

• Industrial applications of AI

• Natural Language Processing (NLP)

• Image processing

• Generative models (LLM, GAN, etc.)

• Trusted AI

• Optimization

• Algorithms

• I am not available full-time, but I can dedicate a significant amount of time to collaborative projects alongside my other professional commitments.

• I prefer remote or online collaborations with researchers or labs.

• Actively contribute to research projects (analysis, modeling, experimentation).

• Co-author and publish meaningful results in conferences or journals.

• Develop in-depth expertise in preparation for my PhD.

If you are working on an interesting project or are looking for a motivated collaborator for research, I would be happy to discuss it. Feel free to contact me directly here or via email for more details.

Thank you for your time, and I look forward to potential collaborations! 😊

会议征稿：2025年人工智能与数字伦理国际学术会议 （ICAIDE 2025）

Call for papers：IEEE 2025 International Conference on Artificial Intelligence and Digital Ethics (ICAIDE 2025), which will be held in Guangzhou,China during May 30-June 1, 2025.

大会官网(投稿网址）：https://ais.cn/u/6JNRr2

大会时间：2025年5月30-6月1日

大会地点：中国--广州

提交检索：EI Compendex、Scopus

2025年人工智能与数字伦理国际学术会议 （ICAIDE 2025）是一个聚集智慧的盛会，期待您能积极参与讨论，共同分享您独特的见解和经验。与您同行的，还有来自包括计算机科学、社会学、法学、哲学等多个学科的顶尖专家。我们相信，各位的参与将为人工智能与数字伦理领域带来新的视角与灵感。

计算机科学与技术

智能科学与技术

机器人工程

软件工程

数学

自动化

数据科学与大数据技术

机械设计及其自动化

模式识别与智能系统

......

科技伦理

科技异化

生命伦理

基因伦理

信息伦理

神经伦理

技术哲学

人工智能

数字伦理

......

所有的投稿都必须经过2-3位组委会专家审稿，经过严格的审稿之后，最终所录用的论文将由IEEE (ISBN:979-8-3315-2385-5) 出版，收录进IEEE Xplore数据库，出版后提交EI, Scopus检索。

1. 仅参会：无任何展示，仅听众参会，可提问交流

2. 参会做口头汇报：时间为8-12分钟，带本次会议logo，PPT汇报

3. 参会做海报展示：A1尺寸，递交电子版高清图，由组委会协助打印

**投稿被录用缴费后，可有一位作者免费参会，上方参会方式，三选一

**本次大会也接收不投稿仅参会

**参加本次会议均可获得：参会照片、参会证明、邀请函等相关资料

Hello everyone,

I’m exploring the role of artificial intelligence (AI) in transforming classroom learning experiences for students with learning disabilities. With the rise of AI-powered adaptive tools and personalized learning platforms, I’m particularly interested in understanding how these technologies can support learners facing challenges such as dyslexia, dysgraphia, ADHD, and other specific learning differences.

I welcome any references, case studies, or personal insights from those working in this field. Thank you for your contributions to this discussion.

The proliferation of multilingual information across the globe necessitates robust and adaptable machine translation (MT) systems. The recent advancement of large language models (LLMs) performed a major transformation that enabled them to understand and produce text across different languages with remarkable skills. The paper evaluates current LLM-based multilingual translation practices while discussing approaches alongside obstacles while outlining prospective developments. The research examines architectural approaches along with training methods and evaluation criteria that evaluate advancements in obtaining global cross-lingual communication systems.

NLP experienced a seminal transformation because of enormous pre-trained language models [4]. The models mBERT [10], XLM [1] and their successors excel at cross-lingual understanding and generation tasks. These models obtain their learning capability by undergoing pre-training using extensive multilingual document collections which allows them to develop unified linguistic components spread across various language systems. Zero-shot cross-lingual transfer becomes possible through shared representations because one model can easily work with different languages without explicit retraining [3, 15].

Multilingual models achieve their premier value from the natural connections that exist between languages according to research [1]. FILTER [1] enables better cross-lingual language understanding through a finetuning process that applies cross-lingual data fusion followed by independent language encodings and a subsequent fusion step for extracting multilingual knowledge. This research outlines an embedding alignment process that measures sentence similarity through pretrained monolingual embedding models for designing soft labels from text similarities [2].

The actual deployment of multilingual machine translation presents multiple complexities based on [4]. Studies indicate that multilingual models achieve success with unknown additional benefits that do not directly contribute to cross-lingual knowledge transfer. The LLM performance shows substantial variations between different languages according to existing research [5].

The achievement of LLM-based multilingual translation depends critically on both the allocated framework architecture as well as training oversight protocols. The transformer-based model architecture along with its attention mechanism has become the leading architecture in the field [10]. The architectural design learns deep associations spanning long sequences and word connections between languages effectively [10].

Pre-training is a crucial step in training LLMs for multilingual translation [3, 6]. Models are typically pre-trained on massive multilingual corpora using tasks like masked language modeling and next sentence prediction [6]. This pre-training phase allows the model to learn general linguistic knowledge and develop a shared understanding of different languages. After pre-training, the model is fine-tuned on specific translation tasks, using parallel corpora or other forms of supervision [1, 3].

Several techniques have been developed to improve the effectiveness of pre-training and fine-tuning. Mixed-lingual pre-training leverages both cross-lingual and monolingual tasks to improve the model's language modeling capabilities [6]. This approach allows the model to benefit from the abundance of monolingual data to enhance its language modeling capabilities, while also learning cross-lingual relationships through translation tasks. Contrastive learning has also been employed, where the model is trained to learn similar representations for sentences that are translations of each other [2, 14].

A significant advantage of LLMs is their ability to perform zero-shot translation, translating between language pairs without any direct training data [3, 15]. This capability is particularly valuable for low-resource languages, where parallel corpora are scarce.

However, the performance of zero-shot translation can be limited [13]. Translation quality often suffers, especially for languages with significant linguistic differences. To address this, researchers have explored various strategies to improve zero-shot translation. One approach involves augmenting the model with additional knowledge, such as bilingual dictionaries or cross-lingual word embeddings [18]. Another strategy is to use images as pivots, enabling the model to learn translations by associating words with visual concepts [11].

For low-resource languages, translation-based approaches have shown promise [13]. These methods involve translating the source language training data and the target language test instances, enhancing the model's ability to learn from limited resources [13]. Furthermore, techniques such as optimal transport distillation can be used to transfer knowledge from high-resource to low-resource languages [16].

The benefits of multilingual LLMs extend beyond direct translation. They have also proven effective in cross-lingual transfer for various downstream tasks, such as question answering, summarization, and information extraction [1, 9, 6, 7].

In cross-lingual question answering, the goal is to answer questions in one language using information from another language [9, 14]. LLMs can be fine-tuned on question-answering datasets in high-resource languages and then applied to low-resource languages, leveraging the shared representations learned during pre-training [14]. Techniques like MuCoT [14] augment the QA samples of the target language using translation and transliteration to improve performance. XOR QA [9] enables questions from one language to be answered via answer content from another, addressing both information scarcity and asymmetry.

Cross-lingual summarization aims to generate a summary in one language for a document written in another [6, 8]. LLMs can be trained to generate summaries in the target language by leveraging the information from the source language document [6]. The mixed-lingual pre-training approach has proven effective in cross-lingual summarization, where the model learns to generate summaries by leveraging both cross-lingual tasks, such as translation, and monolingual tasks, such as masked language models [6].

Cross-lingual open information extraction (OIE) seeks to extract structured information from text across multiple languages [7]. MT4CrossOIE [7] uses a multi-stage tuning framework to enhance cross-lingual OIE by injecting language-specific knowledge into a shared model. This framework uses language-specific modules and prompting techniques to improve performance.

The field of LLM-based multilingual translation is continuously evolving, with researchers exploring various enhancements and applications.

One area of focus is improving the interpretability of LLMs [10]. Understanding how these models make decisions is crucial for building trust and ensuring their responsible use. Studies have investigated the role of attention heads in Transformer-based models, revealing that pruning certain heads can improve performance in cross-lingual and multilingual tasks [10].

Another area of research is the development of more sophisticated prompting strategies [5, 20]. Prompting involves providing the model with specific instructions or examples to guide its behavior. Multi-Lingual Prompt (MLPrompt) [20], for example, automatically translates error-prone rules into another language to improve LLMs' reasoning and understanding. Contrastive alignment instructions (AlignInstruct) [17] emphasizes cross-lingual supervision via a cross-lingual discriminator, improving translation quality in unseen and low-resource languages.

LLMs are also being applied to cross-lingual plagiarism detection [12]. By simulating word embeddings, models can detect plagiarism by reproducing the predictions of online machine translators, even when translated texts are replaced with synonyms [12].

The use of LLMs in multi-modal tasks, such as image captioning, is also gaining traction [8, 19]. Unpaired cross-lingual image caption generation uses self-supervised rewards to address the lack of paired image-caption data for different languages [19].

Furthermore, the quality of translations can still be imperfect, particularly for complex or nuanced text [4, 19]. LLMs may struggle with idiomatic expressions, cultural references, and other subtleties of human language.

Finally, ethical considerations are crucial [4]. The use of LLMs for translation raises concerns about privacy, bias, and misinformation. It is essential to develop and deploy these technologies responsibly, ensuring that they are used to promote understanding and communication, not to exacerbate existing inequalities or spread harmful content.

The field of LLM-based multilingual translation is poised for continued innovation. Several promising directions for future research include:

In conclusion, LLMs have revolutionized multilingual translation, offering unprecedented capabilities in cross-lingual communication. While challenges remain, the field is rapidly evolving, with ongoing research focused on addressing limitations and expanding the scope of these technologies. The future of multilingual translation is bright, with the potential to unlock new opportunities for global communication and collaboration.

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Check out our latest paper discussing the Design and Implementation of a conversational AI solution for Network Management and Orchestration in 5G, 6G and Next Generation Networks!

This work is being further extended to using Large Language Models (LLMs) to achieve simplified Intent-Based management and orchestration paradigms.

ResearchGate Paper Draft Link:

IEEE 2025 International Conference on Artificial Intelligence and Digital Ethics (ICAIDE 2025), which will be held in Guangzhou,China during May 30-June 1, 2025.

This conference aims to bring together thought leaders, researchers, and practitioners from academia, industry, and policy sectors to engage in meaningful discussions about the implications of artificial intelligence on society. We will explore critical topics, including algorithmic bias, data privacy, the impact of AI on employment, and the ethical frameworks necessary for responsible AI development. Through keynote speeches, panel discussions, and workshops, participants will collaborate to address the challenges and opportunities presented by AI technologies. Together, we strive to foster a deeper understanding of digital ethics and its role in shaping a sustainable future. Join us as we navigate the complex intersection of technology and morality in the digital age.

The topics of interest for submission include, but are not limited to:

◕ Track 1: Artificial Intelligence

AI Algorithms

Natural Language Processing

Fuzzy Logic

Computer Vision and Image Understanding

Signal and Image Processing

Speech and Natural Language Processing

Computational Theories of Learning

Information Retrieval and Fusion

......

◕ Track 2: Digital Ethics

AI Algorithmic Transparency

Data Privacy

Bias and Fairness

Ethical Frameworks

AI Governance

Ethics of Machine Learning

AI Decision-Making

Explainability

Accountability and Responsibility

......

All accepted papers will be published by IEEE (ISBN:979-8-3315-2385-5) and will be submitted to IEEE Xplore, EI Compendex, Scopus for indexing.

Full Paper Submission Date: March 15, 2025

Registration Deadline: May 10, 2025

Final Paper Submission Date: April 30, 2025

Conference Date: May 30-June 1, 2025

Please send the full paper(word+pdf) to Submission System:

The drug discovery pipeline is a notoriously arduous, time-consuming, and expensive process. It often takes over a decade and billions of dollars to bring a new drug to market [6]. This challenging landscape has spurred the exploration of computational approaches to accelerate and improve the efficiency of drug development. Machine learning (ML) and, increasingly, quantum machine learning (QML) are emerging as powerful tools, offering the potential to revolutionize various stages of the drug discovery process [10, 19]. This review will explore the current state of the art, highlighting key applications, emerging trends, and future directions in the application of ML and QML to drug discovery, focusing on the generation of novel molecules, prediction of drug-target interactions, drug repurposing, and the mitigation of challenges such as data scarcity and interpretability. This field is rapidly evolving, with new methods and applications constantly emerging, promising to reshape the future of medicine.

Generative Models for Drug Design

One of the most promising applications of ML in drug discovery is the de novo design of drug molecules. Generative models are trained on existing datasets of known drugs and can then create novel molecules with desired properties [4, 19]. These models offer the potential to explore vast chemical spaces and identify promising drug candidates more efficiently than traditional methods [12].

Energy-based generative models, such as the one developed in [1], are designed for target-specific drug discovery. TagMol, the proposed model, generates molecules with binding affinity scores comparable to real molecules. The study also highlights the advantage of using GAT-based models over GCN baselines for faster and better learning. Similarly, [4] proposes the use of various QML techniques, including generative adversarial networks (GANs), to generate small drug molecules.

Variational autoencoders (VAEs) are another popular approach for drug design. These models learn a latent representation of molecular structures and can generate new molecules by sampling from this latent space [11, 18]. However, as highlighted in [11], near-term quantum computers have limitations that hinder the representation learning in high-dimensional spaces. The authors present a scalable quantum generative autoencoder (SQ-VAE) for simultaneously reconstructing and sampling drug molecules, and a corresponding vanilla variant (SQ-AE) for better reconstruction. The results suggest that quantum computing advantages can be achieved for normalized low-dimension molecules, and that high-dimension molecules generated from quantum generative autoencoders have better drug properties within the same learning period. A hybrid quantum-classical deep learning model tailored for binding affinity prediction in drug discovery shows a 6% improvement in prediction accuracy relative to existing classical models, as well as a significantly more stable convergence performance [9]. Moreover, the work in [18] built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer, which could fit a state-of-the-art D-Wave quantum annealer and generate novel chemical structures with medicinal chemistry and synthetic accessibility properties.

Conditional diversity networks offer another approach to drug design. These networks can generate potential drug molecules from a prototype, which is especially valuable in drug discovery where researchers often start from a molecule with some of the desired properties [36].

Predicting Drug-Target Interactions

Identifying drug-target interactions (DTIs) is a critical step in drug discovery. Accurate prediction of these interactions can significantly reduce the time and cost associated with identifying lead compounds [5, 15, 16, 27, 28, 29].

Several studies have explored the use of deep learning models for DTI prediction [5, 16, 28, 29, 30]. DrugMAN, developed in [5], integrates heterogeneous information from multiple biological networks using a mutual attention network. This approach allows the model to capture complex relationships between drugs and targets, leading to improved prediction performance. The study in [16] proposes a method for predicting drug-target binding affinity using deep learning models, using a modified GRU and GNN to extract features from the drug-target protein sequences and the drug molecule map, respectively, to obtain their feature vectors. Another approach, presented in [27], incorporates 3D protein structure features for drug target affinity prediction using GraphPrint. The model generates graph representations for protein 3D structures using amino acid residue location coordinates and combines them with drug graph representation and traditional features to jointly learn drug target affinity, demonstrating that 3D protein structure-based features provide information complementary to traditional features. A cross-field information fusion strategy is employed in [28] to acquire local and global protein information, proposing the siamese drug-target interaction SiamDTI prediction method.

Knowledge graphs and knowledge graph embedding (KGE) models have also shown promise in DTI prediction [15, 21]. In [15], a causal intervention-based confidence measure assesses the triplet score to improve the accuracy of the DTI prediction model. The study in [21] proposes an inductive RGCN for learning informative relation embeddings, even in the few-shot learning regime, which can be applied on the drug-repurposing knowledge graph (DRKG) for discovering drugs for Covid-19. Another study [29] proposes a self-attention-based multi-view representation learning approach for modeling drug-target interactions, achieving competitive prediction performance and offering biologically plausible drug-target interaction interpretations. Furthermore, the study in [30] proposes a convolutional neural network for EEG-mediated DTI prediction, which allows the identification of similarities in the mechanisms of action and effects of psychotropic drugs.

Drug Repurposing

Drug repurposing, the process of identifying new uses for existing drugs, offers a faster and more cost-effective approach to drug discovery compared to de novo drug development [3, 13, 20, 21, 24, 31, 37, 39]. By leveraging existing safety and efficacy data, drug repurposing can significantly accelerate the drug development process [13].

Several studies have explored the use of ML and AI for drug repurposing. NeuroCADR, a novel system for drug repurposing, uses a multi-pronged approach consisting of k-nearest neighbor algorithms (KNN), random forest classification, and decision trees [13]. The system identified novel drug candidates for epilepsy. In [20], a Knowledge Graph-based Machine Learning framework for explainably predicting Drugs Treating Diseases (KGML-xDTD) is proposed, which can achieve state-of-the-art performance in both predictions of drug repurposing and recapitulation of human-curated drug MOA paths. In the context of the COVID-19 pandemic, [37] proposes Dr-COVID, a graph neural network (GNN) based drug repurposing model. The model constructs a four-layered heterogeneous graph to model the complex interactions between drugs, diseases, genes, and anatomies. The study in [31] proposes a multi-agent framework to enhance the drug repurposing process using state-of-the-art machine learning techniques and knowledge integration. Similarly, [39] develops a semi-supervised drug embedding that incorporates two sources of information: (1) underlying chemical grammar that is inferred from chemical structures of drugs and drug-like molecules (unsupervised), and (2) hierarchical relations that are encoded in an expert-crafted hierarchy of approved drugs (supervised).

Self-supervised learning can also be applied to drug repurposing to address label sparsity [24]. The study in [24] proposes a multi-task self-supervised learning framework for computational drug repositioning, which tackles label sparsity by learning a better drug representation. The framework uses data augmentation strategies and contrast learning to mine the internal relationships of the original drug features and a multi-input decoding network to improve the reconstruction ability of the autoencoder model.

Addressing Challenges in Drug Discovery with ML/QML

While ML and QML offer significant promise for drug discovery, several challenges need to be addressed to fully realize their potential. These include data scarcity, the need for interpretability, and the development of methods for handling novel compounds.

Data Scarcity and Cold Start Problems

The availability of high-quality, labeled data is often a limiting factor in ML applications, particularly in drug discovery [24, 25]. Many drug discovery tasks, such as predicting drug-target interactions or drug responses, suffer from data scarcity, making it difficult to train robust and accurate models [8, 24].

To address this challenge, several approaches have been developed. Self-supervised learning techniques can be used to learn representations from unlabeled data, which can then be used to improve the performance of supervised models [24, 26]. Transfer learning, where knowledge learned from related tasks is transferred to the target task, can also be effective [8]. For instance, [8] proposes using transfer learning from chemical-chemical interaction (CCI) and protein-protein interaction (PPI) task to drug-target interaction task to solve the cold start problem. The representation learned by CCI and PPI tasks can be transferred smoothly to the drug-target interaction task due to the similar nature of the tasks. The study in [25] discusses the performance of classical and quantum classifiers in QSAR prediction and attempts to demonstrate the quantum advantages in the generalization power of the quantum classifier under conditions of limited data availability.

Interpretability

Many ML models, particularly deep learning models, are often considered "black boxes," making it difficult to understand why they make certain predictions [2, 17]. This lack of interpretability can be a major barrier to the adoption of ML in drug discovery, as it can be challenging to trust and validate the predictions made by these models.

Explainable artificial intelligence (XAI) techniques are being developed to address this challenge [17]. XAI methods aim to provide insights into the decision-making process of ML models, making them more transparent and understandable. The study in [17] provides a comprehensive overview of the current state-of-the-art in XAI for drug discovery, including various XAI methods, their application in drug discovery, and the challenges and limitations of XAI techniques in drug discovery. The study in [29] proposes a self-attention-based multi-view representation learning approach for modeling drug-target interactions that offer biologically plausible drug-target interaction interpretations. The KGML-xDTD framework in [20] provides KG-path explanations for drug repurposing predictions by leveraging the combination of prediction outcomes and existing biological knowledge and publications.

Handling Novel Structures

Many ML models struggle to generalize to novel chemical structures or biological targets that are not well-represented in the training data [21, 28]. This is particularly problematic in drug discovery, where researchers are often interested in identifying novel drug candidates or targeting previously unexplored proteins.

Several strategies are being explored to address this challenge. Graph neural networks (GNNs), which are designed to handle graph-structured data, are particularly well-suited for modeling molecular structures [21, 26, 27]. The study in [21] proposes an inductive RGCN for learning informative relation embeddings, even in the few-shot learning regime. The cross-field information fusion strategy in [28] is employed to acquire local and global protein information.

Quantum computing offers the potential to overcome some of the limitations of classical ML, particularly in handling complex data and performing computationally intensive tasks [4, 6, 10, 11]. QML algorithms can potentially accelerate drug discovery by enabling more accurate simulations, faster molecular property predictions, and the efficient exploration of chemical space [4, 6, 10].

Several studies have explored the application of QML to drug discovery [4, 9, 10, 11, 18, 25]. For example, [4] proposes a suite of QML techniques to generate small drug molecules, classify binding pockets in proteins, and generate large drug molecules. The study in [10] discusses the theoretical foundations of quantum machine learning, including data encoding, variational quantum circuits, and hybrid quantum-classical approaches. The study in [11] presents a scalable quantum generative autoencoder (SQ-VAE) for simultaneously reconstructing and sampling drug molecules. The study in [18] built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer, which could fit a state-of-the-art D-Wave quantum annealer and generate novel chemical structures with medicinal chemistry and synthetic accessibility properties. The study in [9] introduces a novel hybrid quantum-classical deep learning model tailored for binding affinity prediction in drug discovery. The study in [25] discusses the performance of classical and quantum classifiers in QSAR prediction.

Hybrid quantum-classical approaches, which combine the strengths of both quantum and classical computing, are particularly promising [9, 10, 18]. These approaches can leverage quantum computers for specific tasks, such as molecular simulations, while using classical computers for other aspects of the drug discovery pipeline.

Future Directions

The application of ML and QML to drug discovery is still in its early stages, and significant opportunities remain for future research and development. Several key areas warrant further investigation:

The ongoing convergence of quantum computing and artificial intelligence has the potential to revolutionize the field of drug discovery, leading to faster, cheaper, and more effective treatments for a wide range of diseases [6, 19]. While challenges remain, the rapid pace of innovation in both ML and QML suggests that these technologies will play an increasingly important role in the future of medicine.

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References

IEEE 2025 2nd International Conference on Smart Grid and Artificial Intelligence (SGAI 2025) will be held in Changsha,China from March 21 to 23, 2025.

The topics of interest for submission include, but are not limited to:

1. Smart Grid

· Smart Grid Technology

· Intelligent Sensor

· Grid Interface

· Power and Energy Systems

· Smart Grid and Distributed Energy

· Distribution System and Substation Automation

· Wide Area Protection, Communication, and Control in Energy Systems

· Intelligent Power Equipment

· Smart Grid Deployment

· Power System and Its Automation

· Power System Modeling, Simulation, and Analysis

......

2. Artificial Intelligence

· Artificial Intelligence Algorithms

· Natural Language Processing

· Artificial Intelligence Modeling and Simulation

· AI Chip

· AI Intelligent Operation and Maintenance

· Neural Networks and Complex Systems

· Intelligent Control

· Information and Communication Technology for Smart Grids

All papers will be reviewed by two or three expert reviewers from the conference committees. After a careful reviewing process, all accepted papers will be published in conference proceedings by IEEE and submit to IEEE Xplore, EI Compendex and Scopus for indexing.

Full Paper Submission Date: February 26, 2025

Registration Deadline: March 5, 2025

Final Paper Submission Date: March 13, 2025

Conference Dates: March 21-23, 2025

Please send the full paper(word+pdf) to Submission System:

Hi, I’m a Master’s student in Mathematical Engineering and I’m currently exploring PhD opportunities in Poland and looking for researchers or labs working on AI Safety/Mechanistic Interpretability.

Could anyone recommend researchers, labs, or universities in Poland that focus on these or related areas (ML/NLP)?

Thank you in advance for any guidance or recommendations.

Hello,

I wish to share a few notes I made during a lecture I attended at TU/e, the lecture was about AGI, why it should be disregarded and why the scientific community should focus on specialized models in AI instead.

the discussion triggered in me an idea of an AGI system design I shared it on GitHub https://github.com/Samir-atra/AGI-Lecture-Notes

I hope you enjoy it.

I have developed a dataset for Swahili digraphs, and I need to publish it and its journal.

Publisher:

Emerald Publishing

Book Title:

Data Science for Decision Makers: Leveraging Business Analytics, Intelligence, and AI for Organizational Success

Editors:

· Dr. Miltiadis D. Lytras, The American College of Greece, Greece

· Dr. Lily Popova Zhuhadar, Western Kentucky University, USA

Book Description

As the digital landscape evolves, the integration of Business Analytics (BA), Business Intelligence (BI), and Artificial Intelligence (AI) is revolutionizing Decision-Making processes across industries. Data Science for Decision Makers serves as a comprehensive resource, exploring these fields' convergence to optimize organizational success. With the continuous advancements in AI and data science, this book is both timely and essential for business leaders, managers, and academics looking to harness these technologies for enhanced Decision-Making and strategic growth. This book combines theoretical insights with practical applications, addressing current and future challenges and providing actionable guidance. It aims to bridge the gap between advanced analytical theories and their applications in real-world business scenarios, featuring contributions from global experts and detailed case studies from various industries.

Book Sections and Chapter Topics

Section 1: Foundations of Business Analytics and Intelligence

· The evolution of business analytics and intelligence

· Key concepts and definitions in BA and BI

· Data management and governance

· Analytical methods and tools

· The role of descriptive, predictive, and prescriptive analytics

Section 2: Artificial Intelligence in Business

· Overview of AI technologies in business

· AI for data mining and pattern recognition

· Machine learning algorithms for predictive analytics

· Natural language processing for business intelligence

· AI-driven decision support systems

Section 3: Integrating AI with Business Analytics and Intelligence

· Strategic integration of AI in business systems

· Case studies on AI and BI synergies

· Overcoming challenges in AI adoption

· The impact of AI on business reporting and visualization

· Best practices for AI and BI integration

Section 4: Advanced Analytics Techniques

· Advanced statistical models for business analytics

· Deep learning applications in BI

· Sentiment analysis and consumer behavior

· Realtime analytics and streaming data

· Predictive and prescriptive analytics case studies

Section 5: Ethical, Legal, and Social Implications

· Data privacy and security in AI and BI

· Ethical considerations in data use

· Regulatory compliance and standards

· Social implications of AI in business

· Building trust and transparency in analytics

Section 6: Future Trends and Directions

· The future of AI in business analytics

· Emerging technologies and their potential impact

· Evolving business models driven by AI and analytics

· The role of AI in sustainable business practices

· Preparing for the next wave of digital transformation

Objectives of the Book

· Provide a deep understanding of AI’s role in transforming business analytics and intelligence.

· Present strategies for integrating AI to enhance Decision-Making and operational efficiency.

· Address ethical and regulatory considerations in data analytics.

· Serve as a practical guide for executives, data scientists, and academics in a data-driven economy.

Important Dates

· Chapter Proposal Submission Deadline: 25 November 2024

· Full Chapter Submission Deadline: 31 January 2025

· Revisions Due: 4 April 2025

· Submission to Publisher: 1 May 2025

· Anticipated Publication: Winter 2025

Target Audience

· Business Professionals and Executives: Seeking insights to improve Decision-Making.

· Data Scientists and Business Analysts: Expanding their toolkit with AI and analytics techniques.

· Academic Researchers and Educators: Using it as a resource for teaching and research.

· IT and MIS Professionals: Enhancing their understanding of BI systems and data management.

· Policy Makers and Regulatory Bodies: Understanding the social and regulatory impacts of AI and analytics.

Keywords

· Artificial Intelligence

· Business Analytics

· Business Intelligence

· Data Science

· Decision-Making

Submission Guidelines

We invite chapter proposals that align with the outlined sections and objectives. Proposals should include:

· Title

· Authors and affiliations

· Abstract (200-250 words)

· Keywords

Contact Information

Dr. Miltiadis D. Lytras: miltiadis.lytras@gmail.com

Dr. Lily Popova Zhuhadar: lily.popova.zhuhadar@wku.edu

I created a lecture series for this, please suggest any improvement.

https://youtu.be/6LS3zv9sQlY?si=uUMl2-1RfCGgbW6p

I am an honours student in the Linguistics Department. I am eager to conduct BNLP research and am interested in pursuing a higher education in this discipline (NLP) . How can I begin taking measures to work in this industry outside of my studies? Please help with information or guidance.

[CFP]2024 6th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI 2024) - December

2024 6th International Conference on Robotics, Intelligent Control and Artificial Intelligence (RICAI 2024) will be held in Nanjing, China during December 6-8, 2024. It aims at a key theme on Robotics, Intelligent Control, and Artificial Intelligence. It is a great opportunity to get together and share ideas and knowledge among the Industry experts, researchers and academics.

We invite you to submit your latest research results for presentation. You are also welcomed to suggest any topic, activity or scientific program you would like to see covered during the conference. Your active contribution will be crucial to the success of the conference and its impact on our global mission.

◕Robotics and Intelligent Control

· Robot Structure Design and Control

· Robot Navigation, Positioning and Autonomous Control

· Bio-machine-electric System and Integration

· Intelligent Unmanned System Perception and Control

· Human-machine-environment Natural Interaction and Interaction

......

◕Intelligent Communication In Robotics and Intelligent Control

· Intelligent Communication and Cooperative Control

· Robotics and Advanced Communication Technologies

· Perception and Cognitive Communication

· Self-Organizing Networks and Robotic Clusters

......

◕Intelligent IoT In Robotics and Intelligent Control

· Internet of Things and Intelligent Robotics

· Intelligent Home Systems and Service Robotics

· IoT Architecture and Robotic Operation Platforms

· Edge AI and Its Integration in Robotics

......

Full Paper Submission Date: October 31, 2024

Registration Deadline: November 8, 2024

Conference Dates: December 6-8, 2024

[CFP]2024 4th International Symposium on Artificial Intelligence and Big Data (AIBFD 2024) - December

AIBDF 2024 will be held in Ganzhou during December 27-29, 2024. The conference will focus on the artificial intelligence and big data, discuss the key challenges and research directions faced by the development of this field, in order to promote the development and application of theories and technologies in this field in universities and enterprises, and provide innovative scholars who focus on this research field, engineers and industry experts provide a favorable platform for exchanging new ideas and presenting research results.

◕Track 1：Artificial Intelligence

Natural language processing

Fuzzy logic

Signal and image processing

Speech and natural language processing

Learning computational theory

......

◕Track 2：Big data technology

Decision support system

Data mining

Data visualization

Sensor network

Analog and digital signal processing

......

Full Paper Submission Date: December 23, 2024

Registration Deadline: December 23, 2024

Conference Dates: December 27-29, 2024

Link: https://www.techscience.com/cmc/special_detail/AI_data_analysis

The rapid evolution of Artificial Intelligence (AI), data analysis techniques, and big data has significantly transformed various fields. AI technologies have shown tremendous potential in enhancing various applications, predicting trends, and automating complex tasks. Natural Language Processing (NLP) has also advanced, enabling better understanding and generation of human language. However, these advancements come with challenges such as data privacy concerns, algorithmic biases, and the constant need for adaptation to emerging trends. This special issue aims to explore both the advancements and challenges associated with AI, data analysis, and big data, highlighting their impact on various domains and ensuring robust implementations.

This special issue seeks to provide a comprehensive overview of the latest advancements in AI, data analysis, and big data technologies and their applications across different fields. It aims to present innovative solutions, evaluate their effectiveness, and discuss the challenges faced in implementing these technologies in real-world scenarios

The scope includes but is not limited to :

·  AI-driven prediction and automation systems

·  Advanced data analysis techniques

·  Big data processing and Social Media analysis

·  Natural Language Processing (NLP) advancements and applications

·  Ethical and privacy considerations in AI and data analysis

·  Case studies and real-world applications

·  Challenges in integrating AI with existing systems

·  Future trends and emerging technologies in the field

·  AI and Machine Learning for Predictive Analytics

·  Behavioral Analytics an big data

·  Automated Systems and Process Optimization

·  Big Data Analytics and Processing Techniques

·  Natural Language Processing (NLP) for Various Applications

·  Privacy and Ethical Issues in AI Solutions

·  Data Analysis Techniques for Predictive Modelling

·  Challenges in AI-Enhanced Systems

We would be honored if you could consider contributing to our special issue . I will assist you throughout the submission process to ensure everything proceeds smoothly. You can reach me at elaine.lu@techscience.com for any further information.

We look forward to your valuable contribution to this topic.

[CFP]2024 2nd International Conference on Artificial Intelligence, Systems and Network Security (AISNS 2024) - December

AISNS 2024 is to bring together innovative academics and industrial experts in the field of Artificial Intelligence, Systems and Cyber Security to a common forum. The primary goal of the conference is to promote research and developmental activities in computer information science and application technology and another goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working all around the world. The conference will be held every year to make it an ideal platform for people to share views and experiences in computer information science and application technology and related areas.

◕Artificial Intelligence

· AI Algorithms

· Natural Language Processing

· Fuzzy Logic

· Computer Vision and Image Understanding

· Signal and Image Processing

......

◕Network Security

· Active Defense Systems

· Adaptive Defense Systems

· Analysis, Benchmark of Security Systems

· Applied Cryptography

· Authentication

· Biometric Security

......

◕Computer Systems

· Operating Systems

· Distributed Systems

· Database Systems

Full Paper Submission Date: October 10, 2024

Registration Deadline: November 29, 2024

Conference Dates: December 20-22, 2024

Hi, I have been working on some Natural Language Processing research, and my dataset has several duplicate records. I wonder should I delete those duplicate records to increase the performance of the algorithms on test data?

I'm not sure whether duplication has a positive or negative impact on test or train data. I found some controversial answers online regarding this, which make me confused!

For reference, I'm using ML algorithms such as Decision Tree, KNN, Random Forest, Logistic Regression, MNB etc. On the other hand, DL algorithms such as CNN and RNN.

[CFP]2024 4th International Conference on Digital Society and Intelligent Systems (DSInS 2024) - November

DSInS 2024 will be held in Sydney, Australia during November 20-22, 2024. The conference will focus on the application of Intelligent systems in digital society, discuss the key challenges and research directions faced by the development of this field, in order to promote the development and application of theories and technologies in this field in universities and enterprises, and provide innovative scholars who focus on this research field, engineers and industry experts provide a favorable platform for exchanging new ideas and presenting research results.

Internet of Things Planned highlights of DSInS 2024 include:

● Addresses and presentations by some of the most respected researchers in the Intelligent Systems and Digital Society

● Panel discussions

● Presentations of accepted academic and practitioner research papers; a poster paper session

◕Intelligent Systems

Pattern recognition

Machine learning

Neural networks

Natural language processing

Deep learning

Knowledge graph

......

◕Digital Society

Digital manufacturing

Digital communication

Digital transportation

Digital community

Digital government

......

◕Application of Intelligent systems on Digital Society

Character recognition

Video surveillance

Factory automation

Assistive robotics

Intelligent Fault Diagnosis

......

Final Paper Submission Date: October 25, 2024

Conference Dates: November 20-22, 2024

Introduction to Natural Language Processing (NLP)

Watch now: https://youtu.be/7qJB2WYvr5I

Are you curious about how machines understand and process human language? Dive into the fascinating world of Natural Language Processing (NLP) with this video!

Key topics covered:

What is NLP?

Applications of NLP in real-world scenarios

Key techniques like tokenization, sentiment analysis, and more

NLP in AI and Machine Learning

This video is perfect for beginners who want to explore the basics of NLP and understand its impact on today's AI-driven world.

If you're keen to explore more about AI, Machine Learning, and cutting-edge technologies, don't forget to subscribe to my channel! 💡

#NLP #MachineLearning #ArtificialIntelligence #DataScience #AI #TechEducation #ProfessorRahulJain #YouTubeLearning #NaturalLanguageProcessing

01. What is Machine Learning

02. Types of Machine Learning

03. Understanding Data in Machine Learning

04. Master Data Preprocessing for Machine Learning Clean & Prepare Your Data

05. What is Supervised Learning ?

06. Regression Algorithms | Linear Regression

07.Classification Algorithms Explained | Logistic Regression, Decision Trees, Support Vector Machine

08. What is Unsupervised Learning? Basic Introduction

09. Clustering Techniques | K Means Clustering

10. Simplifying Data with PCA | Principal Component Analysis | Dimensionality Reduction

11. Neural Networks Overview | Understanding layers, Neurons & Activations

12. Training Neural Networks | Weights, Biases, Backpropogation & Gradient Descent

13. What is Reinforcement Learning?

14. Q-Learning & Markov Decision Processes

15. Model Evaluation in Supervised Learning Accuracy, Precision, Recall, F1 Score & Confusion Matrix

16. Overfitting vs Underfitting | How to Recognize & Avoid Them + The Bias Variance Trade off

17. Build a Simple Linear Regression Model | Step by Step Guide Using Real World Data

18. Classification Model using Decision Trees

19. Clustering with K Means Algorithm | Hands on Example

20. Introduction to Deep Learning | How It Differs from Traditional ML + Real World Use Cases

21. Introduction to Natural Language Processing (NLP)

22. Machine Learning Recap, Suggestions for Advanced Topics and Continued Learning

#MachineLearning #AIForBeginners #FreeCourse #LearnAI #ArtificialIntelligence #MLBasics #ProfessorRahulJain #FutureOfLearning #AIEducation #ProfessorRahulJain

How does CFG contribute to the practical applications of natural language processing , particularly in parsing and language recognition? Provide a how CFG facilitates these processes in a real-world scenario.

i have joined as research scholar in computer science. i would like to do my PhD work in NLP domain. Please suggest some of the research topic related to NLP

I am currently in the process of selecting a topic for my dissertation in Data Science. Given the rapid advancements and the increasing number of studies in this field, I want to ensure that my research is both original and impactful.

I would greatly appreciate your insights on which topics or areas within Data Science you feel have been overdone or are generally met with fatigue by the academic community. Are there any specific themes, methods, or applications that you think should be avoided due to their oversaturation in recent dissertations?

Your guidance would be invaluable in helping me choose a research direction that is both fresh and relevant.

Thank you in advance for your assistance!

Title：Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

Journal：Computers, Materials & Continua (SCI IF2.0 CITESCORE5.3)

Link：https://www.techscience.com/cmc/v80n2/57626

Since the 1950s, when the Turing Test was introduced, there has been notable progress in machine language intelligence. Language modeling, crucial for AI development, has evolved from statistical to neural models over the last two decades. Recently, transformer-based Pre-trained Language Models (PLM) have excelled in Natural Language Processing (NLP) tasks by leveraging large-scale training corpora. Increasing the scale of these models enhances performance significantly, introducing abilities like context learning that smaller models lack. The advancement in Large Language Models, exemplified by the development of ChatGPT, has made significant impacts both academically and industrially, capturing widespread societal interest. This survey provides an overview of the development and prospects from Large Language Models (LLM) to Large Multimodal Models (LMM). It first discusses the contributions and technological advancements of LLMs in the field of natural language processing, especially in text generation and language understanding. Then, it turns to the discussion of LMMs, which integrates various data modalities such as text, images, and sound, demonstrating advanced capabilities in understanding and generating cross-modal content, paving new pathways for the adaptability and flexibility of AI systems. Finally, the survey highlights the prospects of LMMs in terms of technological development and application potential, while also pointing out challenges in data integration, cross-modal understanding accuracy, providing a comprehensive perspective on the latest developments in this field.

I'm currently seeking postdoctoral research opportunities in multidisciplinary areas within Computer Science, with an interest in both academic and industry settings. My research interests include advanced cloud-based data management for smart buildings, NLP for low-resource languages like Amharic, AI and machine learning, data science and big data, human-computer interaction, and robotics. I'm open to discussing potential opportunities and collaborations in these fields. Please feel free to contact me if you are aware of any suitable positions.

Hello!

I am currently trying to reproduce the results described in "Inferring psycholinguistic properties of words" (https://www.aclweb.org/anthology/N16-1050.pdf), by implementing the authors' bootstrapping algorithm. However, for some unknown reason I keep getting correlations with the actual ratings in the .4-.6 range, rather than the .8-.9 range. If you have a software implementation that reaches the performance levels from the paper, could you please share it with me?

Many thanks,

Armand

会议征稿：【中国算力大会主办】2024算法、高性能计算与人工智能国际学术会议（AHPCAI 2024）定于2024年6月21-23日在中国郑州举行。会议主要围绕算法、高性能计算与人工智能等研究领域展开讨论。

大会官网（投稿网址）：https://ais.cn/u/AvAzUf​

会议时间：2024年6月21-23日

会议地点：中国·郑州

接受/拒稿通知：投稿后1周左右

收录检索：EI Compendex，Scopus

1、算法（算法分析/近似算法/可计算性理论/进化算法/遗传算法/数值分析/在线算法/量子算法/随机化算法/排序算法/算法图论和组合/计算几何/计算技术和应等）

2、人工智能（自然语言处理/知识表现/智能搜索/机器学习/感知问题/模式识别/逻辑程序设计软计算/不精确和不确定的管理/人工生命/神经网络/复杂系统/遗传算法/计算机视觉等）

3、高性能计算（网络计算技术/高性能计算软件与工具开发/计算机系统评测技术/云计算系统/移动计算系统/点对点计算/网格和集群计算/服务和互联网计算/效用计算等）

4、图像处理 （图像识别/图片检测网络/机器人视觉/聚类/图像数字化/图像增强和复原/图像数据编码/图像分割/模拟图像处理/数字图像处理/图像描述/光学图像处理/数字信号处理/图像传输等）

5、其他相关主题

1、 本会议投稿经过2-3位组委会专家严格审核之后，最终所录用的论文将由SPIE - The International Society for Optical Engineering (ISSN: 0277-786X)出版，出版后提交EI Compendex, Scopus检索。

*AHPCAI前三届已完成EI检索！稳定且快速！

2、本届会议作为2024算力大会分会进行公开征稿，高质量论文将遴选至2024算力大会，由IEEE出版，见刊后由期刊社提交至 EI Compendex和Scopus检索。

1、作者参会：一篇录用文章允许一名作者免费参会；

2、主讲嘉宾：申请主题演讲，由组委会审核；

3、口头演讲：申请口头报告，时间为10分钟；

4、海报展示：申请海报展示，A1尺寸，彩色打印；

5、听众参会：不投稿仅参会，也可申请演讲及展示。

6、报名参会：https://ais.cn/u/AvAzUf

Abstract submission deadline: 18th June, 2024

Submission link: https://www.nextgenaiconference.com/submission.php

The International Conference on Generative AI and its Applications (ICGAIA) is a prestigious event that brings together researchers, practitioners, and experts from academia, industry, and government organizations to discuss the latest advancements, trends, and challenges in the field of generative artificial intelligence (AI) and its various applications. ICGAIA serves as a platform for presenting cutting-edge research papers, sharing insights, exchanging ideas, and fostering collaborations among professionals working in areas such as machine learning, deep learning, natural language processing, computer vision, robotics, and more.

IEEE 2024 5th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE 2024) will be held on September 20-22, 2024 in Wenzhou, China.

The topics of interest include, but are not limited to:

· Big Data Analysis

· Deep Learning、Machine Learning

· Artificial Intelligence

· Pattern Recognition

· Data Mining

· Cloud Computing Technologies

· Internet of Things

· AI Applied to the IoT

· Clustering and Classificatio

· Soft Computing

· Natural Language Processing

· E-commerce and E-learning

· Wireless Networking

· Network Security

· Big Data Networking Technologies

· Graph-based Data Analysis

· Signal Processing

· Online Data Analysis

· Sequential Data Processing

All papers, both invited and contributed, the accepted papers, will be published and submitted for inclusion into IEEE Xplore subject to meeting IEEE Xplore’s scope and quality requirements, and also submitted to EI Compendex and Scopus for indexing.

Full Paper Submission Date: July 10,2024

Registration Deadline: August 5, 2024

Final Paper Submission Date: August 20, 2024

Conference Dates: September 20-22, 2024

Please send the full paper(word+pdf) to Submission System:

Dear All,

*Call for Papers:*

The *Annual International Conference on Recent Trends in Healthcare Innovation*, often abbreviated as AICRTHI, stands as a pivotal event on the global healthcare calendar. This conference is organizing by *Department of Information Science & Engineering, Vidyavardhaka College of Engineering*, Mysuru, Karnataka, India.

We invite submissions of original research, case studies, and innovative ideas for presentation at the *AICRTHI-2024.* Selected papers presented at the conference will have the opportunity to be included in the conference proceedings of *CRC Press, Taylor & Francis Group (Scopus Indexed).*

The conference aims to cover a wide range of topics related to machine learning and computing systems, including but not limited to :

• Pharmaceutical Research and Drug Discovery

• Patient-centric care and Personalized Medicine

• Healthcare Analytics and Population Health Management

• Case Studies and Real-World Applications

• AI in Healthcare Operations and Management

• Support Systems (CDSS)

• Electronic Health Records (EHR) and Data Integration

• Natural Language Processing (NLP) in Healthcare

• Telemedicine and Remote Patient Monitoring

• Healthcare Robotics and Automation

*Important Dates:*

*Submission Deadline: 30th July 2024*

*Notification of Acceptance: 15th September 2024*

*Conference Dates: 24th and 25th October 2024*

*Submission Link*: https://cmt3.research.microsoft.com/AICRTHI2024

For inquiries or assistance regarding paper submissions, please contact

Dr. Prema N S, at premans@vvce.ac.in.

We look forward to receiving your submissions and welcoming you to *AICRTHI-2024!*