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Smartserve – Leveraging Generative AI for Multifaceted Services

Smartserve – Leveraging Generative AI for Multifaceted Serv...

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Innovation in Technology (INOCON), ieee international conference for

作者： Deepali Kayande Sachin Malave Abhishek Jagtap Omkar Ghaduse Vidul Bhosale Tanvi Choudhari Department of Computer Engineering A.P. Shah Institute of Technology Thane India

SmartServe, an integrated AI platform, seeks to address the scattered nature of current AI platforms by efficiently integrating multiple services. To transform user interaction, this all-encompassing solution uses modern deep learning and natural language processing techniques. With the help of OpenAI’s generative AI model, SmartServe is able to generate on-demand text, code and images in response to feedback from users. Additionally, the platform features powerful text summarizer and a multilingual translator that uses natural language processing (NLP) to provide cross-cultural and accessible data synthesis is improved Notably with the inclusion of JSearch, a job search feature that collects listings from several portals. To ensure a seamless and efficient AI experience, SmartServe’s integrated approach streamlines user interaction and takes financial considerations into account Users can purchase credits for targeted service usage.

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Curriculum Vitae Sorting: A Novel Framework for Personality-based Automatic CV Sorting using Deep Learning 5

Curriculum Vitae Sorting: A Novel Framework for Personality-...

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5th international conference on Computers and Artificial Intelligence Technology, CAIT 2024

作者： Patel, Nikhil Trivedi, Sandeep Bhalani, Vijay Faruqui, Nuruzzaman University of Dubuque IA United States Deloitte Consulting LLP Houston United States University of Southern California Department of Petroleum Engineering Los Angeles 90007 CA United States Daffodil International University Daffodil Smart City Department of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798331530891

Both skill and personality play impactful roles in professional performance. The Human Resource Management (HRM) identifies and verifies the skill set and academic background while recruiting new employees. However, analyzing the personalities of the applicants is challenging. Because humans have intrinsic characteristics that allow them to express fabricated personalities in different settings. Nevertheless, people frequently express their true sentiments on social media. This paper presents an innovative and effective framework, the Curriculum Vitae Sorting (CVS) framework, that uses Bidirectional Long Short-Term Memory (BiLSTM) and the Myers-Briggs Type Indicator (MBTI) dataset to identify the personalities of job applicants using their social media posts. The CVS framework achieves a remarkable 92.88% classification accuracy with a 4.55% False Positive Rate (FPR). The practical application of this framework demonstrates an 11.67% improvement in the Key Performance Indicator (KPI) among newly recruited employees. The 0.9311% precision, 0.9294% recall, and 0.9403% F1-score of the CVS framework demonstrate its outstanding and reliable performance in personality classification. This unique application of Deep Learning (DL) in HRM unearths a new dimension of Artificial Intelligence (AI) in business, helping organizations recruit employees with the required personalities and qualities. © 2024 ieee.

关键词： BiLSTM Network CV Sorting Deep Learning Myers-Briggs Type Indicator natural language processing Neural Network Personality Classification

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Rights reach: AI-powered legal assistance for the physically challenged 5th

Rights reach: AI-powered legal assistance for the physically...

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5th international conference on Advances in AI for Biomedical Instrumentation, Electronics and Computing, ICABEC 2023

作者： Bittal, Vijaylaxmi Pingale, Sakshi Shahade, Makarand Patil, Manish Patil, Ketaki Patil, Manashri Alfatmi, Khalid Department of Computer Engineering SVKM’s Institute of Technology Maharashtra Dhule India

ISBN: (纸本)9781032642987

Many physically challenged people struggle to obtain the necessary legal assistance. They can find it difficult to understand their rights or obtain aid within the current legal system. They require a specific AI-powered computer program to assist them with legal matters. So that they can receive the care they need and integrate into society like everyone else, this program should be simple for them to use and comprehend. The revolutionary effects of artificial intelligence (AI) on improving legal accessibility for people with disabilities or handicaps are explored in this system. AI technologies provide specialized solutions by addressing the special difficulties that each person encounters when traversing the legal system. These include adaptable forms for legal material, communication tools supported by AI, and the benefits they get from our laws. AI integration has the potential to level the playing field for those who have disabilities in the judicial system, ensuring fair access to justice by knowing their legal rights and benefits given to them by the government, just like employment laws, educational laws, traveling benefits, etc. for the physically challenged. The envisioned chatbot will take the form of a versatile application that can run on mobiles. It’s being developed using Xamarin, ensuring smooth operation on Android. The system commences with voice input, which is then converted to text through speech-to-text technology. Following this, tokenization and keyword extraction techniques are applied to identify essential terms in the input. Subsequently, natural language processing (NLP) is utilized to restructure the input sentence, enhancing its understanding. These sentences are used to formulate queries, subsequently employed to search in a manually created knowledge containing legal information and benefits applicable to individuals with physical challenges at last the system transforms the textual information into spoken words, ensuring that the re

关键词： Artificial intelligence

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Ensemble Learning-based Image Captioning with NLP for Context-Aware Descriptions

Ensemble Learning-based Image Captioning with NLP for Contex...

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2025 international conference on Intelligent Computing and Control Systems, ICICCS 2025

作者： Naidu, P.M. Dhanesh Sai, S. Naveen, M. Kumar, Ch. Anil Vignan Institute of Technology and Science Department of Computer Science and Engineering (AI & ML) Telangana Nalgonda India

ISBN: (纸本)9798331512088

Image captioning is a challenging task that requires an appropriate combination of computer vision and natural language processing to describe an image accurately and with context relevance. This paper presents an advanced method for captioning images using an ensemble learning setup along with an NLP-based fusion layer. The proposed system analyzes images from various perspectives by using multiple pre-trained models to capture diverse features, and the generated captions are then combined into a single coherent description using the fusion layer. The system's efficiency is demonstrated using popular evaluation metrics such as BLEU, ROUGE, and METEOR, showing high accuracy and computational efficiency. This system is designed to work efficiently for a variety of image types, ensuring scalability and real-time application compatibility. The proposed method addresses common challenges in image captioning, including ambiguity, context preservation, and ensuring relevance across different datasets. Qualitative evaluation confirms the system's ability to produce human-like captions, making it suitable for a wide range of applications such as assistive technologies, autonomous systems, and content generation. © 2025 ieee.

关键词： Captioning Convolutional Neural Network Deep Learning Image Captions Image Feature Extraction

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(NMRNN-LSTM) - Novel Modified RNN with Long and Short-Term Memory Unit in Healthcare and Big Data Applications

(NMRNN-LSTM) - Novel Modified RNN with Long and Short-Term M...

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2022 international conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2022

作者： Deepa, N. Prabakeran, S. Devi, T. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Department of Networking and Communications Chengalpattu District Kattankulathur India

ISBN: (纸本)9781665461092

In the modern world, people's expectations and needs are automatically supportive and easy to use such as voice messages, playing music, or movies automatically which may reduce the manual operations mostly. In past decades technological advances such as machine learning and its application over many data like structured and unstructured are very much tedious. Whereas the operations based on non-categorical data, and categorical data are working rapidly using natural language processing (NLP) comparatively, existing ones were not very productive. Each process on the internet is carrying an enormous amount of information which can lag in storage as well as performance. When any CRUD operations such as create, modify, update and delete are being analyzed one at a time, complex data such as unstructured and structured data are used in any field. In such a way the location analysis, social media data, health organization information, etc are categorized in natural language processing (NLP). The proposed work is organized as i) managing the huge amount of data in healthcare and log files created due to electronic health record management(EHR), ii) Unstructured data that are generated from all electronic equipment such as monitoring heartbeat, brain waves, etc that can be interpreted to classify using machine learning algorithms. To overcome the complications and medical records access inefficiency due to the complex structure of the dataset, natural language processing uses the recurrent neural network along with the novel modified long and short-term memory unit (NMRNN-LSTM). Using the big data types such as structured, unstructured, and reinforcement kind of databases which handle images such as CTs, X-rays, MRI, raw texts, video streaming medical history to have effective systems and clinical records for enhancing the technological Medical care. © 2022 ieee.

关键词： Records management

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Recent Advancement in Transformer Based Electric Load Forecasting: A Review

Recent Advancement in Transformer Based Electric Load Foreca...

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Smart and Sustainable Developments in Electrical engineering (SSDEE), ieee international conference on

作者： Verendra Singh Kharkwal S. C. Gupta Department of Electrical Engineering M.A.N.I.T. Bhopal India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

Load forecasting (LF) is an integral aspect of power system planning, operations, maintenance, and management. Widespread development of advanced metering infrastructure (AMI) with smart meter adoption has made availability of granular load datasets possible. AI Transformer models have been extensively used for natural language processing (NLP) task. Their capability to learn temporal dependencies from large time series dataset have led to significant research in transformer based time series forecasting. In a decentralized power system with increasing renewable energy sources (RESs) penetration and prosumer participation, LF becomes a complex time series forecasting problem. In recent times, Transformer based model variants have shown to model complex temporal dependencies with greater accuracy. This work surveys pertinent literature to present recent advancement in Transformer based LF models. Simulation studies on benchmarked dataset have shown superior performance of transformer based LF models for varied data granularity and different prediction length as compared to benchmark Deep Learning (DL) models. Model innovations to tackle transformer bottlenecks like space & time complexity are also explored. To conclude, the up-to-date review tries to address open problems and highlights possible future research directions.

关键词： Deep learning Load forecasting Reviews Time series analysis Predictive models Transformers natural language processing Data models Forecasting Load modeling

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The Design and Analysis of a Storytelling Chatbot with natural language processing Techniques for Enhancing EFL Reading

The Design and Analysis of a Storytelling Chatbot with Natur...

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international conference on Advanced Learning Technologies (ICALT)

作者： Yong Ting Feng Chen-Chung Liu Chia-Hui Chang Graduate Instittute of Network Learning Technology National Central University Taoyuan Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

This study explored the applications of natural language processing techniques on the analysis of storybook to design the storytelling chatbots. Two natural language processing techniques, co-reference and dependency parsing techniques, were applied to process the text of 157 stories books as a means to implement in the invite-narration, positive recognition, follow-up strategies when the chatbot retells the story with the students. The results of the study found that 19 (42%) students demonstrated full narratives which can re-tell the overall story in the storybook, while 22 (49%) students could only retell a simplified story. Affordances and limitations of the use of the two techniques in developing chatbots for reading are discussed.

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A Proposed Algorithm to Perform Few Shot Learning with different sampling sizes 4

A Proposed Algorithm to Perform Few Shot Learning with diffe...

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4th ieee international conference on Advances in Electronics, Computers and Communications, ICAECC 2022

作者： Dedhia, Kashvi Konkar, Mallika Shah, Dhruvil Tawde, Prachi Dwarkadas J Sanghvi College of Engineering Department of Information Technology Mumbai India

ISBN: (纸本)9781665402392

Often times there is scarcity when it comes to model training of a quality dataset. Sometimes the data that is available is unlabelled, sometimes very few samples are available for some classes. In these cases, few shot learning comes in handy. There are two approaches to few shot learning Data Level approach and Parameter Level approach. The paper consists of analysis of the number of training samples using parameter level approach. Two classes have been used to perform few shot learning. Meta transfer learning is being used, by initialising the parameters of convolutional neutral networks (CNN) learner model from a model trained on ImageNet. It has been performed incrementally on datasets of various sizes. The results and performance of all the models are compared to the results when the entire dataset is used. As well as the advantages of using few shot learning. It has found its applications in a wide range of fields mainly computer vision, natural language processing etc. © 2022 ieee.

关键词： Learning systems

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Analysis of Semi-Supervised Algorithms in natural language processing 1

Analysis of Semi-Supervised Algorithms in Natural Language P...

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1st international conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2021

作者： Zhang, Ting Ma, Yanzhou Li, Hongxin Information Engineering University Basic Department Henan Luoyang China Information Engineering University Teaching Evaluation Center Henan Luoyang China

ISBN: (纸本)9781665413008

With the rapid development of modern science and technology, the application space of natural language processing technology is constantly expanded, and good application results are achieved in various industry fields. In particular, the use of semi-supervised algorithms can not only reduce the cost of natural language processing technology, but also improve the processing efficiency. Based on this, the article focuses on the practical application of semi-supervised algorithms in natural language processing, firstly, it elaborates the connotation and important value of semi-supervised algorithms, secondly, it introduces the main application techniques of semi-supervised algorithms, and finally, it proposes the practical application effects of such techniques in natural language processing, hoping to provide reference and reference for relevant researchers. © 2021 ieee.

关键词： natural language processing systems

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Combining Ontological knowledge and Large language Model for User-Friendly Service Robots

Combining Ontological Knowledge and Large Language Model for...

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ieee/RSJ international conference on Intelligent Robots and Systems (IROS)

作者： Haru Nakajima Jun Miura Department of Computer Science and Engineering Toyohashi University of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Lifestyle support through robotics is an increasingly promising field, with expectations for robots to take over or assist with chores like floor cleaning, table setting and clearing, and fetching items. The growth of AI, particularly foundation models, such as large language models (LLMs) and visual language models (VLMs), is significantly shaping this sector. LLMs, by facilitating natural interactions and providing vast general knowledge, are proving invaluable for robotic tasks. This paper focuses on the benefits of LLMs for "bring-me" tasks, where robots fetch specific items for users, often based on ambiguous instructions. Our previous efforts utilized an ontology extended to handle environmental data to resolve such ambiguities, but faced limitations when unresolvable ambiguities required user intervention for clarity. Here, we enhance our approach by integrating LLMs for providing additional commonsense knowledge, pairing it with ontological data to mitigate the issue of hallucinations and reduce the need for user queries, thus improving system usability. We present a system that merges these knowledge bases and assess its efficacy on "bring-me" tasks, aiming to provide a more seamless and efficient robotic assistance experience.

关键词： Visualization Service robots Foundation models Large language models knowledge based systems Ontologies Usability Intelligent robots Floors Commonsense reasoning

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