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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Srinivasa Rao Adapa Himanshu Gupta Susmoy Mukherjee Praneeth Reddy Mallupalli Priyanka Avhad Kamal Upreti Software Engineer OpenLogix LLC Michigan USA Software Engineer Meta New Jersey USA Electrical and Electronics Engg. VIT Institute Tamil Nadu India Software Engineer Arcesium Hyderabad India Computer Engineering Veermata Jijabai Technological Inst. Mumbai India Dept. of Computer Science Christ University Delhi NCR Campus India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Within the domain of human-robot communication, effective communication is paramount for seamless and smooth collaboration between humans and robots. A promising method for improving language grounding is reinforcement learning (RL), which enables robots to translate spoken commands into suitable behaviors. This paper presents a comprehensive review of recent advancements in RL techniques applied to the task of language grounding in human-robot interaction, focusing specifically on instruction following. Key challenges in this domain include the ambiguity of natural language, the complexity of action spaces, and the need for robust and interpretable models. Various RL algorithms and architectures tailored for language grounding tasks are discussed, highlighting their strengths and limitations. Furthermore, real-world applications and experimental results are examined, showcasing the effectiveness of RL-based approaches in enabling robots to understand and execute instructions from human users. Finally, promising directions for future research are identified, emphasizing the importance of addressing scalability, generalization, and adaptability in RL-based language grounding systems for human-robot interaction.

关键词： Grounding Reviews Scalability Human-robot interaction Collaboration Reinforcement learning Oral communication Robustness Noise measurement Robots

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News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models 3

News-Driven Stock Price Forecasting in Indian Markets: A Com...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Attaluri, Kaushal Tripathi, Mukesh Kumar Reddy, Srinithi Shivendra Software Engineer-Machine Learning and AI Pegasystems India Worldwide Pvt Ltd Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Vasavi College of Engineering Department of Information Technology Hyderabad India D. K. College Department of Computer Application Bihar Dumraon India

ISBN: (纸本)9798350354171

Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (AI) and natural language processing (NLP) has significantly advanced stock market forecasting. AI's ability to analyze complex data sets allows for more informed predictions. Despite these advancements, stock price forecasting remains an area where AI has not yet achieved optimal results. In this paper, we forecast stock prices using 30 years of historical data from various national banks in India sourced from the National Stock Exchange. We employ advanced deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM and Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Additionally, we analyze news data from tweets and reliable sources like Business Standard and Reuters, recognizing their significant impact on stock price movements. © 2024 IEEE.

关键词： Contrastive Learning

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An In-Depth Study on Handwritten Signature Authentication Using Unique Writer Traits 1

An In-Depth Study on Handwritten Signature Authentication Us...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Vamsikrishna, M. Murari, Haritha Manikandan, M. Mishra, Nilamadhab Raghavendra Gupta, T. Patel, Rohit Aditya University Dept. of Computer Applications Kakinada India Spark Infotech Inc Sr Software Engineer PhoenixAZ United States VeL Tech. R Dr.S Dept. of Computer Science and Engineering Tamilnadu India VIT Bhopal University Dept. of Computer Science and Engineering Sehore India HITM Telenagana Hyderabad India Calorx Teachers' University Dept of CSE Gujrat India

ISBN: (纸本)9798350356816

Handwriting verification, a subset of signature verification, involves using handwritten text to verify or authorize someone's identification. Although simple handwriting can serve as a signature, it is often modified to suit the signer's preferences. The uniqueness of each person's handwritten signature makes it essential for accurate distinction from others. This study presents a handwritten signature verification system using a writer-dependent approach. While signature verification is widely used for identity verification and security, it is susceptible to forgeries. The writer-dependent approach considers the unique characteristics of an individual's signature, training the system to recognize them and making forgery replication more challenging. An extensive review of the literature on signature verification systems, focusing on the writer-dependent approach, was conducted. A system was then developed and implemented based on this approach, utilizing a dataset of signatures from 12 writers. Results indicate that the writer-dependent approach outperforms other methods in terms of accuracy and robustness. The system demonstrated high precision and recall rates, underscoring its effectiveness in detecting both genuine and forged signatures. This study highlights the writer-dependent approach's efficacy in enhancing the accuracy and security of signature verification systems, with implications for developing more reliable and efficient identity verification systems in today's digital age. © 2024 IEEE.

关键词： Authentication

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Mental Workload Classification from fNIRS Signals by Leveraging Machine Learning

Mental Workload Classification from fNIRS Signals by Leverag...

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2023 IEEE Signal Processing in Medicine and Biology Symposium, SPMB 2023

作者： Hasan, M. Mahmud, M. Poudel, S. Donthula, K. Poudel, K. University of Memphis Department of Electrical and Computer Engineering TN United States Senior Software Engineer Optum Inc MurfreesboroTN United States Middle Tennessee State University Computational and Data Science MurfreesboroTN United States Middle Tennessee State University Computer Science MurfreesboroTN United States

ISBN: (纸本)9798350341256

Mental workload (MWL) identification is vital to know human cognitive functioning, performance, and well-being. In this work, we develop models for identifying low vs. high MWL using different genres of machine learning classifiers. We used non-invasive functional near-infrared spectroscopy (fNIRS) signals while participants classified the low vs. high levels of MWL tasks. Our analysis shows the low vs. high MWL can be identified best from the whole brain data. The k-nearest neighbors classifier showed the best performance with an accuracy of 98.8%, an area under the curve (AUC) of 98.8%, F1 score, precision, and recall of 98.0% from the whole brain data without overlapping signals. A separate hemisphere analysis using left hemisphere (LH) and right hemisphere (RH) activity showed that the LH activity has better classification ability than the RH activity. We also examined the classification with the top six features that could identify the low vs. high MWL with an accuracy of 97.4%, (AUC) 97.4%, F1 score, precision, and recall of 97.0%. These findings would be useful for developing more intuitive and user-friendly interfaces in the human-computer interface. © 2023 IEEE.

关键词： Infrared devices

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Predicting Modalities of Dyslexic Students using Neuro-Linguistic Programming to Enhance Learning Method

Predicting Modalities of Dyslexic Students using Neuro-Lingu...

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2022 IEEE International Conference on Data science and Information System, ICDSIS 2022

作者： Choube, Gaurav Dudhmande, Gauri Rahul Pushparaj, Jagalingam Anand, Christopher Suresh, Shilpa Software Engineer Optum Global Solutions Pvt Ltd Hyderabad India Software Engineer Intel Technology Pvt Ltd Aurangabad India Vellore Institute of Technology School of Computer Science and Engineering Vellore India McMaster University Department of Computing and Software Ontario Canada Manipal Institute of Technology Department of Mechatronics Manipal India

ISBN: (数字)9781665498012

ISBN: (纸本)9781665498012

Dyslexia causes difficulty in reading, writing and learning. The children at a tender age have always suffered due to dyslexia. Dyslexia deceives student's perception and makes it difficult in the process of learning. In this paper, machine learning techniques like multi-layer perceptron, Decision tree and Gaussian NB approaches were implemented for the prediction of modalities. To enhance the learning approach for the students suffering from dyslexia, the predicted modalities can be adopted. The sampled data was trained, and the target labels were classified into three classes as visual, auditory, and kinesthetic. The data was processed and fed into multi-layer perceptron, decision tree and naive bayes machine learning algorithms using scikit-learn. Confusion matrix was used to evaluate the performance measure of the algorithms. It was observed that models achieved accuracy of 81.41% for MLP Classifier, 63.82% for Decision tree and 79.25% for Naive bayes. The best result was achieved by MLP Classifier. © 2022 IEEE.

关键词： Decision trees

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Cement Strength Prediction using Regression Techniques 10

Cement Strength Prediction using Regression Techniques

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Dewan, Ritu Polishetty, Rohith Sharan, Bhagwati Goyal, Mayank Kumar Balde, Amadou Mouctar Greynoon, Ameen Mohamad Ahmad Galgotias College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Sr. Software Engineer Migo India Limited Hyderabad India Srm University-AP School of Engineering and Sciences Department of Computer Science and Engineering Amaravati India Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India

ISBN: (纸本)9789380544472

One of the primary structural materials in the development business is concrete. Compressive strength is the most frequently utilized metric when assessing the strength and nature of cement. This paper clarifies how to make use of technical regression (RT) methodology to decide the legitimate dose of the concrete mixture. Water, Cement, Fly Ash, Blast Furnace, Superplasticizer, Coarse, and Fine Aggregate, were used as input parameters, on the other hand, compressive strength was used as an output parameter. The produced models were evaluated using statistical parameters: Linear Regression, Decision Tree Regression, and Random Forest Regression. In contrast with other applicable models, it was found that the Gaussian process regression model based on the function of the nucleus of radial bases delivered relatively better results. The recommended models should get a good deal on saving money on materials, labor, and time while also improving accuracy. The proposed concrete should be more durable and accordingly more cost-effective. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Cements

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A Comprehensive Analysis of Deep Learning Frameworks for Gastrointestinal Tract Image Segmentation

A Comprehensive Analysis of Deep Learning Frameworks for Gas...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Shivam Batra Varun Kamath Priyadarshini R Prasham Titiya Manigandan Ramadasan Ronit Naik Software Engineer 1 JPMorgan Chase & Co Bengaluru India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

A critical aspect of medical imaging for accurate diagnosis and treatment of gastrointestinal disorders. This study addresses the importance of this topic and aims to fill the existing research gap. By employing four distinct models, including CNN, UNet, LinkNet, and SegNet, we achieved exceptional results with $99 \%$ accuracy and an IoU score of 0.95. Our findings significantly contribute to the field of image analysis and segmentation, improving the accuracy and efficiency of gastrointestinal endoscopy image segmentation. This research has the potential to improve the uses of medical imaging, leading to more precise diagnosis and better patient outcomes. Overall, this work contributes to the progress of gastrointestinal endoscopy image segmentation, with potential applications to the medical imaging domain.

关键词： Deep learning Image segmentation Accuracy Image resolution Image analysis Endoscopes Gastrointestinal tract

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Crop protection and disease detection using artificial intelligence and computer vision: a comprehensive review

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Multimedia Tools and Applications 2025年第7期84卷 3723-3743页

作者： Shah, Kanish Sushra, Rajat Shah, Manan Shah, Dhairya Shah, Haard Raval, Megh Prajapati, Mitul Software Engineer Walmart SunnyvaleCA United States Atmiya Vidya Mandir Gujarat Surat India Department of Chemical Engineering School of Energy Technology Pandit Deendayal Energy University Gujarat Gandhinagar India Robotics ECE Concentration Northeastern University BostonMA United States Department of Computer Science Concentration in AI Hofstra University HempsteadNY United States Department of Chemical Engineering S. S Agrawal Institute of Engineering & Technology Gujarat Navsari India R &D Engineer Span Inspection Systems Pvt. Ltd Gujarat Gandhinagar India

The technological advancements in the field of agriculture have increased to a great extent in recent years, and many techniques have evolved from other techniques. Some methods are improved or upgraded from the previous versions by implementing a new model or using better hardware devices. This has been helpful for the farmers in increasing crop productivity, and the life expectancy of crops has also increased as the diseases inside or outside the crops can be detected much earlier, and learning at an early stage helps prevent other crops. In this paper, we have presented a study where many varieties of fruits and vegetables have been taken to determine which method was used for a particular crop. By analyzing the various works carried out by the authors, it was inferred that most of the works revolved around image processing and hyperspectral imaging. Due to this, we had also included most of the papers, particularly as the models and hardware components used were much better than other works. Then, a comparative study was done where different fruits and vegetables highlighted the two main areas: the method used and the accuracy obtained. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Crops

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Different Models of Transliteration - A Comprehensive Review

Different Models of Transliteration - A Comprehensive Review

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Innovative Data Communication Technologies and Application (ICIDCA), International Conference on

作者： Mahima Yadav Ishan Kumar Ayush Kumar Department of Computer Science and Engineering Lovely Professional University Phagwara India Software engineer Noida India

Accessing information because of language barriers has grown more crucial as globalization accelerates. The field of study called machine transliteration transforms words from one language to another while preserving their phonetic properties. With the rise of the Internet and the rapid expansion of online data, Information Retrieval (IR) has become a more significant field. Foreign terms are increasingly being incorporated into languages due to the development of new technologies and the flood of information available online. This generally indudes changing the adopted word's orthographical form as well as its original pronunciation to match the phonological norms of the target language. Transliteration describes this phonetic “translation” of a foreign language. Machine translation and cross-language information retrieval both benefit from transliteration. Ambiguity, variability, and out-of-vocabulary (OOV) words are a few of the crucial challenges that transliteration systems must deal with. This paper represents the transliteration models of the last decade used for transliteration-related research carried out by researchers.

关键词： Training Support vector machines Hidden Markov models Training data Transforms Phonetics Information retrieval

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Handwriting Analysis for Classification of Human Personality

Handwriting Analysis for Classification of Human Personality

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International Conference on Advances in Information Technology (ICAIT)

作者： Bipin Nair B J Koushik K S Ankitha Suraj Nithya K Pranav Venkitesan Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Mysuru India Software Engineer Frontend Zenloop GMBH Berlin

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

This paper explores handwriting analysis for personality classification, leveraging machine learning algorithms like KNN, SVM, Naive Bayes, Decision Trees, and Random Forest. We have Extracted features such as stroke pressure, letter size, slant, and spacing from a diverse dataset, the study compares algorithm performance, showing promising results in accuracy, precision, recall, and F1-score. Implications for psychology, machine learning, and handwriting analysis are discussed. In our study, the results show that models such as SVM achieved over 95% accuracy, followed closely by KNN with more than 94% accuracy, indicating significant progress in data analysis and Classification modeling.

关键词： Support vector machines Accuracy Machine learning algorithms Psychology Nearest neighbor methods Feature extraction Prediction algorithms Bayes methods Decision trees Random forests

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