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Auto Text Correction Using NLP Techniques

Auto Text Correction Using NLP Techniques

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Nalla Srivarsha Gummadi Nithin Shanmugasundaram Hariharan Bibhuti Bhusan Dash Subrata Chowdhury Sudhansu Shekhar Patra Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India School of Computer Applications Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India Dept. of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies(A) Chittoor Andhra Pradesh India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Auto text correction is a fundamental tool in modern Natural Language Processing (NLP) that upgrades user involvement by decreasing typographical and linguistic errors in composed content. This extends leverages progressed NLP procedures to create a strong and proficient auto-correction framework that precisely predicts and corrects content input errors in real-time. The framework utilizes a combination of deep learning models, such as sequence-to-sequence models and transformers, in conjunction with conventional strategies like n-grams and edit distance algorithms. By analyzing the context and structure of input sentences, the model is able to offer relevant rectifications, making it versatile to different dialects and user composing styles. Moreover, we investigate diverse assessment measurements and compare different calculations to optimize the precision and productivity of the correction system. The arrangement has potential applications in word processors, mobile keyboards, and other content input platforms where high accuracy and low latency are crucial.

关键词： Productivity Program processors Keyboards Linguistics Transformers Prediction algorithms Natural language processing Real-time systems Low latency communication Mobile computing

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Prostate Cancer Detection Using Integrated Multi Modal Approaches

Prostate Cancer Detection Using Integrated Multi Modal Appro...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Manjula Gururaj Rao Priyanka H Ahamed Shafeeq B M K Hemant Kumar Reddy Depatment of Computer Science and Engineering Nitte (Deemed To Be University) Nmam Institute Of Technology Nitte India PES University Bengaluru India Dept. of CSE Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of CSE VIT AP University Inavolu India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Cancer is one of the terminal diseases that significantly affects an individual's health. Tumours are created when aberrant cells proliferate and divide out of control, posing a threat to neighbouring tissues and organs. Early detection through regular screenings and check-ups can make cancer more treatable. Lots of people throughout are impacted by prostate cancer, the cancer in males that is most frequently diagnosed. The proposed model is a multimodal hybrid approach that utilizes data from .csv files containing prostate attributes and prostate images. It employs both ML and DL models to classify the data as cancerous or non-cancerous and can further stage the cancerous cells into four stages. The proposed method uses the majority voting system to identify the cancerous or non-cancerous. The model achieves an overall accuracy of 81.48 %, with the ML model at 77.8% and the DL model at 87%.

关键词： Support vector machines Accuracy Computational modeling Medical treatment Nearest neighbor methods data models Classification algorithms Prostate cancer Medical diagnostic imaging Tumors

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Spice Vision: Deep Learning Enhanced Spice Classification System

Spice Vision: Deep Learning Enhanced Spice Classification Sy...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Laxmi Pujari Meenaz Belavatgi Mahantesh M. Sajjan Vishwanath Kamatar Pradeep Surasura Nausheen Ammanagi Dept. of Master of Computer Applications K.L.E. Institute of Technology Hubballi India Dept. of Computer Science and Engineering K.L.E Institute of Technolog Hubballi India

ISBN: (数字)9798350364569

ISBN: (纸本)9798350364576

Spices are admired for their distinctive flavors, fragrances, and health advantages. They are an essential part of industrial uses, medical procedures, and culinary customs. The automatic classification of 15 different varieties of Indian spices which are essential to both Indian food and traditional medicine is the main subject of this study. To guarantee quality and consistency, these spices underwent a thorough data preparation procedure after being painstakingly gathered. Then, Convolutional Neural Network (CNN) models were trained and tested using the preprocessed dataset. As a family of deep learning algorithms, CNNs are particularly good at classifying images because they extract hierarchical characteristics directly from the input. The study highlights the possibility for automated systems to precisely detect and classify distinct spices based on their visual properties and investigates the efficacy of various CNN architectures for spice classification. Gathering and preprocessing photos of the spices was part of the model creation procedure. Based on how well these models classified the test images, their performance was assessed. The accompanying picture illustrates the findings, which demonstrate the different degrees of accuracy attained by each model.

关键词： Deep learning Visualization Technological innovation Accuracy Quality assurance Neural networks Manuals Convolutional neural networks Biomedical imaging Resilience

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Improved Ensemble Classification Method for Thyroid Disease Using data Mining Technologies 5

Improved Ensemble Classification Method for Thyroid Disease ...

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5th International Conference on Trends in Electronics and Informatics, ICOEI 2021

作者： Parimala, S. Vadivu, P.Senthil Srm Institute of Science and Technology Dept. of Computer Science Chennai India Hindustan College of Arts and Science Dept. of Computer Applications Coimbatore India

ISBN: (纸本)9781665415712

data mining plays an important role in various healthcare services. The most complicated task is to identify the disease and diagnosing it at a prompt phase. One of the most important functions of the health-care providers is to diagnose illness and save lives. The main aim of any healthcare domain is to discover and diagnose the disease at premature stages with higher accuracy. For providing the treatment in low cost, data mining is considered as the best technique. In healthcare industry, the usage of automated diagnostic systems has become more common in recent years. These systems offer several advantages in the diagnosis process. This paper will explain the aspects of Feature Engineering to solve the problem of dealing with large data sets. Also, the proposed research work has used the concept of feature engineering to solve the issue of large data sets with the algorithm of Instance Selection that combines the two algorithms of Instance Selection using drop1 algorithm and Instance Selection algorithm using Boosting. © 2021 IEEE.

关键词： data mining

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Image encryption using XOR-based continuous tone MSS and Cellular Automata

Image encryption using XOR-based continuous tone MSS and Cel...

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International Conference on Information Systems and computer Networks (ISCON)

作者： Akanksha Hitendra Garg Shivendra Shivani Dept. of Computer Engineering and Applications GLA University Mathura India Dept. of Computer Science Thapar Institute of Engineering and Technology Patiala India

Online communication has become quicker and simpler as a result of the growth of the internet and the advancement in technology. People are using multimedia data in almost all spheres of life. The amount of multimedia data transmitted has been increasing at a massive rate. Multimedia data delivered over open and unreliable networks are susceptible to unauthorized access. As a result, a cryptographic standard is needed to securely and efficiently transmit multimedia data such as images over the networks. This study uses cellular automata and an XOR-based continuous tone MSS approach to create an image encryption method. The suggested approach has eliminated VC’s drawbacks, including secret/share quantity constraints, unpredictable share patterns, codebook requirements, and pixel extension in shares and recovered secret pictures. The recommended approach uses an (n,n) multi-secret sharing system that can send n secret photos simultaneously. At the time of encoding, the master share overcomes been made using the secret key, and it may be successfully retrieved when decoding using a comparable secret key. The exploratory results and security examination shows the proficiency of the given approach. The complexity of the proposed approach can be adjusted by using the different combinations of cellular automata rules.

关键词： Visualization Privacy Automata Encoding computer networks Encryption Decoding

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Textual Sentimental Classification using Convolution Neural Network Algorithm

Textual Sentimental Classification using Convolution Neural ...

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2022 International Conference on Computing, Communication and Power technology, IC3P 2022

作者： Kavitha, K. Chittineni, Suneetha Andhavarpu, Naveen Kumar Chappa, Vasundhara Acharya Nagarjuna University Dept. of Computer Science & Eng. Guntur India Aditya Institute of Technology and Management Department of Information Technology Tekkali India Rvr & Jc College of Engineering Department of Computer Science and Applications Guntur India

ISBN: (纸本)9781665425254

Sentiment analysis on classification of text data is one of the emerging tasks in natural language processing compared to others. In the main, there is a need for big dig for meaningful information from the data present on the internet through sentiment analysis. Deep learning has already been successful in keeping up with sentiment analysis tasks utilizing deep learning models. Nowadays, client happiness is the most crucial factor for every mature firm. As a result, many businesses live and promote their products and services on social media platforms, where they eventually receive evaluations and feedback from their customers. Studying each word and phrase one at a time is time-consuming. Thus, evaluating the sentiment of all texts provides organizations with an overview of how positive and negative users are on a given issue. In this study, we investigated word embedding architecture utilizing the convolutional neural network algorithm or approach (CNN). We created a convolutional neural network architecture using a continuous loop of the tensor flow technique, along with word2vec and evaluated the outcome by using the measures accuracy, precision, recall, and f1-scoring. It reaches an accuracy of more than 92 percent, significantly boosting text categorization accuracy. © 2022 IEEE.

关键词： Sentiment analysis

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Advancing Eye Disease and Glaucoma Diagnosis: A Comprehensive Survey of Deep Learning, Machine Learning, and Hybrid Approaches

Advancing Eye Disease and Glaucoma Diagnosis: A Comprehensiv...

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Smart Technologies, Communication and Robotics (STCR)

作者： B. Venkata Suresh Reddy Bhanu Prakash Battula Computer Science & Engineering JNTUK Kakinada Kakinada India Dept. of CSE-Data Science KKR & KSR Institute of Technology & Sciences Guntur India

ISBN: (数字)9798350357530

ISBN: (纸本)9798350357547

Glaucoma stands as a serious global health issue among eye diseases because the condition leads to permanent blindness when proper diagnosis and treatment are not made in sufficient time. The detection and classification capabilities for these conditions have been significantly improved by recent developments in computational techniques that combine deep learning and traditional machine learning and hybrid models alongside clinical assessments. The survey explores 54 research studies regarding eye disease diagnosis which the authors divide into four distinct sections. Deep learning algorithms particularly CNN variants achieved outstanding success (93.5%) when used for retinal image segmentation together with disease classification tasks. Hybrid models equipped with machine learning and deep learning capabilities reached the maximum accuracy level at 95% for diagnosing diseases. SVMs and other traditional machine learning algorithms delivered accurate results for structured datasets through their performance while medical evaluations helped identify disease trajectories and disease elements. The study presents a thorough overview of the advantages along with drawbacks and potential applications for these analysis techniques in detecting and treating glaucoma along with other eye conditions.

关键词： Deep learning Glaucoma Surveys Image segmentation Machine learning algorithms Accuracy Retina Trajectory Robots Medical diagnostic imaging

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Multidimensional Impacts of Generative AI and an In-dept. Analysis of LLMs with Their Expanding Horizons in technology and Society

Multidimensional Impacts of Generative AI and an In-Depth An...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ramu, K. Nijhawan, Ginni Lalitha, G. Praveen Asha, V. Fakher, Ahmed J. Velammal Institute of Technology Dept. of Cse TN Chennai India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Hyderabad Dundigal India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India New Horizon College of Engineering Department of Computer Applications Bangalore India National University of Science and Technology College of Science Department of Forensic Sciences Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

Generative AI and large language models (LLMs) have transformed technology and society, as this paper details. Generative AI advanced AI greatly. Because they can read and write, LLMs, a form of Generative AI, have altered how humans and robots communicate. This study focuses on how these technologies influence schooling, healthcare, finance, and morals. Recent studies and case studies will help us understand how LLM is applied in many sectors. Our key concerns with these activities are efficiency, efficacy, and morality. The technique also polls and interviews professionals to learn about LLM usage and issues. LLM is quicker and more precise in translating languages, creating content, and analyzing data. Sadly, prejudice, privacy, and abuse issues persisted. LLMs are versatile enough to be utilized in odd fields like individualized learning and mental health treatment, according to the research. LLMs and creative AI can advance technology and society. Despite their benefits, they pose moral and practical issues that must be addressed. This study seeks a compromise between the two LLM methods. A more moral, research-focused use might make them more beneficial and less hazardous. © 2024 IEEE.

关键词： Differential privacy

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The YOLOv8 Edge: Harnessing Custom datasets for Superior Real-Time Detection 18

The YOLOv8 Edge: Harnessing Custom Datasets for Superior Rea...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ahmed, Tafreed Maaz, Ahmad Mahmood, Danyaal Abideen, Zain Ul Arshad, Usama Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still a gap in the research for real-time detection of custom objects with high accuracy and speed. This research addresses this gap by training a YOLOv8 detector on a custom dataset of objects and evaluating its performance on real-time video streams which is by far the latest model and thus is faster and more accurate. Our experimental results demonstrate that our custom-trained YOLOv8 detector achieves high accuracy and real-time performance on a custom dataset of objects. The detector achieved an overall mAP50 of 0.864 and a mAP50-95 of 0.758, with individual class results ranging from 0.47 to 0.995. These findings show that custom training data and YOLOv8 are effective in real-time object detection, which has practical applications in various fields. The significance of the results and our contribution lies in demonstrating the effectiveness of custom training data for improving object detection accuracy and speed using YOLO, which has implications for a wide range of real-world applications. © 2023 IEEE.

关键词： Object detection

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A New Rhythm in A1: Convolutional Neural Networks for Music Genre Classification 18

A New Rhythm in A1: Convolutional Neural Networks for Music ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Javed, Huzaifa Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient AI model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface. The model effectively extracts audio features like Mel Frequency Cepstral Coefficients (MFCCs), zero-crossing rate, and tempo. Testing results reveal strong performance in genre prediction across diverse tracks, affirming the model's ability to discern unique characteristics of various music genres. This performance not only attests to the model's capability in discerning the unique characteristics inherent to different music genres but also suggests that it can effectively generalize to novel, unseen data. Our model lays the groundwork for future enhancements and demonstrates the potential of AI in transforming the music industry - from personalized music playlists to exploratory recommendation systems. The success of this model paves the way for more intricate applications of AI within music analysis. © 2023 IEEE.

关键词： Convolution

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