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Augmenting Content Retrieval Using NLP in AIML 4

Augmenting Content Retrieval Using NLP in AIML

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Shrivastava, Kapil Quezada, Angeles Sharma, Karuna Marquez Lobato, Bogart Yail Vashishth, Nitish Gla University Department of Computer Engineering & Applications UP Mathura India Tijuana Baja California México Computer Science & Engineering Sdgi Global University Tecnológico Nacional de México Campus UP Ghaziabad India Computer Science & Engineering Sdgi Global University UP Ghaziabad India Electronics & Communication Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India

ISBN: (纸本)9798350364729

The paper Augmenting Content Retrieval Using NLP in AIML describes using Natural Language Processing (NLP) with Artificial Intelligence Markup Language (AIML) to enhance content retrieval. Among Markup languages, AIML is most commonly used in chatbots or conversational UI. On the other hand, NLP is a subfield of artificial intelligence that handles reading and understanding human language. Their abstract underscores the importance of improved content extraction for AIML systems that rely on a keyword-based approach, which wouldn't work very well as it fails to capture all elements in human language. Here, wide-scale use of the NLP techniques provides a breakthrough in the analysis. It allows understanding the meaning inherent in natural language and interpreting semanticity through context-sensitive, syntactic rule-based arrangement. The paper proposes a mix of NLP and AIML for better retrieval. Approach: In this technique, we first preprocess the user input using NLP techniques, i.e., POS tagging & Named Entity Recognition, to extract context-specific Keywords/Concepts. Then, these keywords and concepts are mapped with answers in the AIML-based system. The abstract also presents the experimental evaluation of the proposed strategy, which significantly outperforms conventional keyword-based retrieval for content. This proves that applying NLP in AIML systems can even increase the capability to understand and recover user inputs. © 2024 IEEE.

关键词： Knowledge representation

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Map Reduce Techniques for Machine Learning in Big Data Analytics 7

Map Reduce Techniques for Machine Learning in Big Data Analy...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kukreti, Sanjeev Al-Jawahry, Hassan M. Shrivastava, Anurag Bansal, Saloni Raju, Hemanth Begum, Rowsonara Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India Gla University Department of Computer Engineering and Applications Uttar Pradesh Mathura India New Horizon College of Engineering Department of Mechanical Engineering Karnataka Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

One of the most groundbreaking approaches to the processing and analysis of large datasets has been created as a result of the combination of MapReduce techniques and machine learning algorithms within the framework of big data analytics. The problems of traditional data processing in terms of handling the volume, velocity, and variety of big data are addressed by the distributed computing architecture introduced by MapReduce. This research investigates the ways in which MapReduce may be used to simplify the process of machine learning, with a particular focus on the capabilities of the framework to manage enormous volumes of data and to parallelize operations. By contrasting the performance of MapReduce with that of more traditional methods, the study solves the problems of scalability and computing efficiency that are brought about by massive amounts of data. Through an in-depth analysis, the paper highlights the optimization potential of MapReduce as well as its implications for next-generation machine learning applications across a variety of industries. © 2024 IEEE.

关键词： MapReduce

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Risk Modelling and Prediction of Financial Management Using FTL-CSGRU Approach 1

Risk Modelling and Prediction of Financial Management Using ...

引用

1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Mothilal Nehru, G. Kudare, Roopali P. Karnawat, Amrita Tatia Rohini, J. Singh, Saurabh Nishant, Neerav SRM UNIVERSITY Faculty of Science & Humanities Department of computer Applications Chengalpattu India PCET's S B Patil Institute of Management Department of MBA Maharashtra Nigdi Pune India Indira School of Business Studies Tathawade Maharashtra Pune India Erode Sengunthar Engineering College Computer Science and Engineering Tamil Nadu Perundurai India Prestige Institute of Management and Research Department of Finance Indore India School of Engineering Babu Banarasi Das University Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798350371802

The need of building information technology is becoming more evident to those working in corporate financial management as the digital era arrives. An increasing number of modern societies are using enterprise resource planning (ERP) systems for their financial management needs. That way, companies can collect financial data in real-time and analyze it later. The steps of preprocessing, feature extraction, and training the model are all sequentially laid out. When it comes to statistical approaches used for data preprocessing, z-score normalization, decimal scaling, and min-max all offer very accurate results. You get the greatest results with MCML when you compare it to NCA, PCA, and Cluster-cluster LDA, the four approaches that comprise feature extraction. The suggested method employed an FTL-CSGRU strategy for model training. This method seems antiquated in comparison to the cutting-edge FTL and GRU. The statistics showed a significant improvement with an accuracy rating of 97.36%. © 2024 IEEE.

关键词： Enterprise resource planning

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Transfer Learning with ResNet50 for Robust Traffic Sign Recognition: Addressing Challenges in Lighting, Occlusion, and Class Imbalance 2

Transfer Learning with ResNet50 for Robust Traffic Sign Reco...

引用

2nd International Conference on IT Innovations and Knowledge Discovery, ITIKD 2024

作者： Nair, Rekha R Kishore, S. Santhosh, Nandana Babu, Tina Ramamoorthy, H. Alliance School of Advanced Computing Alliance University Department of Computer Science and Engineering Bengaluru India Nitte (Deemed to Be University) Department of Computer Applications Nitte Institute of Professional Education Mangalore India

ISBN: (纸本)9798350355468

The efficacy of deep learning models, specifically Convolutional Neural Networks (CNNs), in accurately recog- nizing and clas- sifying a diverse range of traffic signs. The research utilizes a meticulously curated dataset obtained from Kaggle, comprising annotated images labeled with corresponding ClassIds representing various traffic sign categories. The moti- vation behind this work stems from the critical need for robust and reliable traffic sign recognition systems, which play a vital role in enhancing safety, efficiency, and enabling autonomy in transportation systems. Through systematic experimentation and rigorous validation, the study assesses the performance of CNN models across various evaluation metrics, shedding light on the effectiveness of these deep learning architectures in accurately discerning nuanced visual cues present in traffic sign imagery. Furthermore, the research explores the challenges posed by factors such as variations in lighting conditions, occlusions, and class imbalances, proposing potential mitigation strategies. The proposed methodology utlized transfer learning model ResNet50 which achieved accuracy of 92. © 2025 IEEE.

关键词： accuracy CNN Deep learning efficiency ResNet50 Transfer learning

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Optimizing Random Forest Algorithms for LargeScale Data Analysis 7

Optimizing Random Forest Algorithms for LargeScale Data Anal...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Bhadula, Shuchi Adnan, Myasar Mundher Kumar, Rakesh Rana, Ajay Kaliyaperumal, Gopal Rao, Bolleddu Devananda Boob, Nandini Shirish Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering & Applications Mathura India Amity University Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350350067

The exponential growth of data in recent years has necessitated the development of more efficient and scalable machine learning algorithms to handle large-scale data analysis. Random Forest (RF) algorithms, known for their robustness and accuracy, find extensive use in the realms of classification and regression. However, their performance can be significantly hindered when dealing with massive datasets due to increased computational complexity and resource demands. This paper presents a comprehensive approach to optimizing Random Forest algorithms for large-scale data analysis. We explore various strategies, including data partitioning, algorithmic changes and parallel processing to make RF algorithms more efficient and scalable. Additionally, we introduce novel techniques for feature selection and hyperparameter tuning to improve model performance. Extensive experiments conducted on multiple large-scale datasets demonstrate the effectiveness of the proposed optimizations in reducing computation time and memory usage while maintaining or even enhancing predictive accuracy. From genomics to finance, the optimised Random Forest method shows promise as a robust tool for large-scale data processing in many applications, according to the findings. © 2024 IEEE.

关键词： Data accuracy

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Smart vegetable cutter for Smart Home 1

Smart vegetable cutter for Smart Home

引用

1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Singhal, Saurabh Mangal, Deepak Kumar, Rakesh Sharma, Bhisham Dhaou, Imed Ben GLA University Department of Computer Engineering & Applications Mathura India Jain University Department of Computer Science and IT Bangalore India Dar Al-Hekma University Hekma School of Engineering Computing and Informatics Department of Computer Science Jeddah Saudi Arabia Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665472753

Modern society strives for simpler, more efficient, dependable, speedier, and more automated lifestyle. Existing vegetable cutters in the market have drawbacks such as high investment costs, additional manpower, and time consumption. Moreover, none of them provide the feature of cleaning, peeling, and cutting in a single machine. The veggies offered in the market have pesticides and chemicals, and many people, in their haste, do not wash them properly, endangering their health. To reduce human labor while also providing good health care, a solution to this problem is required. This paper offers a novel method for developing a vegetable cutter and chopper using an Android application and IoT devices. In addition, smart vegetable cutters clean, peel, and cut veggies all in one piece of equipment. This strategy will assist human society in reducing the amount of additional manpower needed and would save them time. © 2023 IEEE.

关键词： Real time systems

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Integrating Random Forest and Support Vector Regression Models for Optimized Energy Consumption Evaluation in Cloud Computing Data Centers 3

Integrating Random Forest and Support Vector Regression Mode...

引用

3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Shaik, Nazeer Kumar, B. Vinoth Bhati, Anita Kumar, Sandeep Singh, Jagendra Sahu, Dinesh Prasad SRIT-Autonomous Department of Computer Science Engineering Anantapur India Ksr Institute for Engineering and Technology Dept. of Electronics and Communication Engineering Tiruchengode India Jecrc University Department of Computer Applications Jaipur India Raj Kumar Goel Institute of Technology Department of Information Technology Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350342338

This study proposes a novel approach for energy consumption evaluation in cloud computing data centers. The immense energy consumption of these data centers presents significant environmental and economic challenges. Traditional evaluation methods often fail to provide accurate estimations due to the complex dynamics of cloud computing environments. The research introduces an integrated model, combining Random Forest Regression and Support Vector Regression, along with a Gradient Boosting Regressor for iterative training of regression parameters. We measure the model's performance using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), replacing traditional relative deviation measures. The proposed approach aims to provide more accurate and efficient energy consumption evaluations, offering potential benefits in energy management, operational costs reduction, and minimizing environmental impact. © 2023 IEEE.

关键词： Cloud Computing Energy Consumption Evaluation Gradient Boosting Regressor Random Forest Regression Support Vector Regression

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Leveraging Transfer Learning for Robust Medical Image Segmentation 4

Leveraging Transfer Learning for Robust Medical Image Segmen...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Tyagi, Priya Sharma, Avadhesh Kumar Masson, Palak Manwal, Manika Goel, Pawan Kumar Varshney, Neeraj Nitra Technical Campus Department of CSE-AIML UP Ghaziabad India Gl Bajaj Institute of Technology and Management Electronics and Communication Engineering Uttar Pradesh Greater Noida201306 India Computer Science & Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India Computer Science & Engineering Graphic Era Hill University R/S Graphic Era Deemed to Be University Uttarakhand Dehradun India Gla University Department of Computer Engineering and Applications UP Mathura India

ISBN: (纸本)9798350364729

Transfer mastering is a powerful approach that has been applied to various tasks in many fields, including pc imaginative and prescient and herbal language processing. This paper describes the software of Switch gaining knowledge of medical photo segmentation, a complex project of appropriately isolating character objects from a scientific picture. This approach involves taking the parameters learned from a pre-educated deep mastering community and adapting them to a new scientific picture segmentation undertaking. The proposed method is tested on three unique datasets offering an expansion of abdominal, brain, and cardiac images. Outcomes display that the proposed technique produces higher segmentation outcomes than conventional deep getting-to-know fashions educated from scratch and might extensively reduce the time required to train and optimize those fashions. Furthermore, the proposed method can even outperform end-to-stop networks in some instances. Therefore, the proposed switch gaining knowledge of the method has the ability for used for strong medical picture segmentation. © 2024 IEEE.

关键词： Convolutional neural networks

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NodeMCU-based Smart Car Parking System for Real-Time Monitoring and Efficient Space Utilization 2

NodeMCU-based Smart Car Parking System for Real-Time Monitor...

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2nd International Conference on IT Innovations and Knowledge Discovery, ITIKD 2024

作者： Nair, Rekha R Kishore, S. Babu, Tina Alliance School of Advanced Computing Department of Computer Science and Engineering Alliance University Bengaluru India Nitte Institute of Professional Education Department of Computer Applications Nitte (Deemed to Be University Mangalore India

ISBN: (纸本)9798350355468

The rapid increase in vehicle ownership has created a pressing demand for larger parking areas and efficient parking management solutions. This study proposes an innovative NodeMCU-based smart car parking system to address the challenges of efficient parking space management i n urban areas. The system utilizes NodeMCU technology, built on the ESP8266 micro-controller, to provide real-time monitoring and optimize parking space utilization. The architecture incorporates NodeMCU devices equipped with sensors to detect vehicle presence in individual parking spots, wirelessly transmitting data to a central server for processing and analysis. The server implements robust data processing and storage mechanisms, along with a user-friendly interface for easy retrieval of parking information. This approach aids in efficient parking space management and helps reduce illegal parking incidents. By providing real-time data and easy-to-use interfaces, the system helps drivers quickly locate available spots, potentially decreasing traffic congestion and parking-related frustration. The proposed smart parking solution aims to streamline parking management, improve urban mobility, and Improve the entire parking experience for drivers in increasingly crowded metropolitan locations. This research contributes to the development of smart city infrastructure, offering a practical solution to modern parking challenges. © 2025 IEEE.

关键词： accuracy CNN Deep learning efficiency ResNet50 Transfer learning

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Protein-Ligand Interaction Prediction with Effective Method of Graph Attention Networks and Long Short-Term Memory 2

Protein-Ligand Interaction Prediction with Effective Method ...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Deepa, N. AlFatlawy, Ramy Shashidhara, K.S. Revathi, R. Reka, M. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Applications Sona College of Arts and Science Salem India

ISBN: (纸本)9798331504960

Nowadays, the drug discovery field of molecules in protein-ligand interactions developing effective therapeutics for identifying potential drug candidates with spatial features to predict binding affinity. However, the existing Deep Learning (DL) based prediction approaches failed to interact and predict the binding affinity of protein-ligand precisely for drug design with binding affinity. To overcome this problem, a Graph Attention Networks with Long Short-Term Memory (GATN-LSTM) method is proposed with spatiotemporal forecasting for interaction between protein and ligand in binding affinity prediction for drug design. The proposed GATN-LSTM method is employed on DUD-E dataset consists of enhanced the prediction by capturing relationship (interaction) between protein and ligand in molecules for drug discovery using PubChem for prediction. Then, the preprocessed data of protein-ligand interaction is extracted using (CNN-GNN) for better generalization of both local and global features for predicting drug discovery. Finally, GATN-LSTM is employed for interaction of molecules with binding affinity which enhanced the drug design and also improved the prediction results. Experimental results of the proposed GATN-LSTM method attained accuracy (0.9992), F1-score (0.9985) are higher than existing prediction approaches such as Convolutional Neural Network (CNN) and Mutual Information-based Feature Selection (MIFS). © 2024 IEEE.

关键词： Graph neural networks

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