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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Ravikumar, S. Vijay, K. Pavithra, S. Prithi, S. Kannan, Sriram Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineeirng Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Chennai India SRM IST School of Computing Department of Computational Intelligence Kattankulathur India Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350348217

A Bike rental expectation conjectures the interest for bikes rentals in reliance of climate conditions like the temperature and calendric data. In bike rental frameworks, it is vital that the managers have to realize the number of bikes to be placed in each and every station. This include empowers them in or-ganizing the necessary number of bikes at each station and choose whether a specific station needs to have additional number of bikes or it is not re-quired. In this venture we will foresee the no of bikes required for explicit climate and climatic conditions. © 2023 IEEE.

关键词： Machine learning

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Analyzing the Evolution of Modern Tamil Script for Natural Language Processing 1

Analyzing the Evolution of Modern Tamil Script for Natural L...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Kaladevi, R. Revathi, A. Manju, A. Department of Computer Science and Engineering Saveetha Engineering College Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9781607685395

History is essential for understanding the society's former state. It is beneficial to understand people's historical status, culture, and how we view the present. The society benefits from the society's diversified culture and unparalleled civilization. The limited sources available to learn about our history are books, epigraphs, and inscriptions. Almost 60% of the Indian inscriptions are in Tamil and only 50% among them are published.. The name India is derived from Indus River and the most vibrant and progressive Indus Valley Civilization is paramount of ancient world. Hinduism, Buddhism, Sikhism, and Jainism are four well-known religions that emerged from this culture. Epigraphs are the most reliable source of information about ancient India's life, culture, religion, and politics. This study examines the evolution of India's various scripts and languages. Discovering the incremental changes in scripts and languages that demonstrate the linguistic relationship between distinct populations is critical. The ability to recognize letters in a variety of scripts, languages, and tools is a boon for natural language processing. © The Electrochemical Society

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Deep Learning for Plant Disease Detection and Crop Yield Prediction based on NPP-WPF Analysis in Smart Agriculture 7

Deep Learning for Plant Disease Detection and Crop Yield Pre...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Amudha, S. Bindu, G. Sankar, Sasi Rekha Poongothai, E. VijayaLakshmi, K. Sarveshwaran, Velliangiri Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai602203 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350341485

Despite a sharp rise in population, agriculture still provides food for all people and acts as a backbone of the country. Nowadays, no proper guidance given to farmers and they are not able to analyze the nature of soil, disease of plant and its requirements. This leads to the depletion of farming in future. Crop diseases must be identified and prevented in order to increase the productivity. To overcome this, the nature of the soil is analyzed through various parameters like Nitrogen-Potassium-Phosphorus Content in soil, Humidity, Temperature, pH value of the soil, Rainfall using sensors, plants disease using cameras in real-time environment. These parameters are obtained using various IoT Sensors, cameras and existing Data, are analyzed through ML Algorithms. The proposed system consists of two modules in crop yield prediction system using the features NPP-WPF whereas NPP stands for Nitrogen-Potassium-Phosphorus and WPF stands for Water Content-Pesticide-Fertilizer, the Farmers can be guided with the details of the crop that is suitable for the soil and about Fertilizer, Pesticide, Water requirements. The second module called as automatic disease detection system uses CNN, ResNet, VGGNet, and Mobile net models to recognize and diagnose plant diseases from their leaves at the early stage. The proposed model is implemented with the dataset of 14 different species of plants and with the categorical classes of 38 classes. The result analysis shows that the suggested model provides accuracy of 98.2% when compared to the existing state-of-the-art methodologies. © 2023 IEEE.

关键词： Phosphorus

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Exploring Research Opportunities to Apply Data Mining Techniques in Software engineering Lifecycle 1

Exploring Research Opportunities to Apply Data Mining Techni...

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1st International Conference on Advances in Electrical, Electronics and computational Intelligence, ICAEECI 2023

作者： Anitha Gracy, J. Parthasarathy, S. Bagavathilaksmi, R. Thiagarajar College of Engineering Applied Mathametics and Computational Science Madurai India VELS Institute of Science Technology and Advanced Studies Department of Computer Application/Information Technology Chennai India

ISBN: (纸本)9798350342796

This Software engineering (SE) is the core research area of the software industry, and numerous algorithms and frameworks are proposed every day. Software engineering is the discipline that keeps up with recent developments in fields and techniques such as data mining, machine learning, natural language processing, and artificial intelligence. It is difficult to manage such a massive amount of data in a software engineering repository if data processing and big data analytics are not available. This paper provides an in-depth study of data mining techniques that to improve the efficiency of the software development lifecycle. The study emphasizes the practical applications and discoveries made by text mining, clustering, classification, and other methods. © 2023 IEEE.

关键词： Classification Clustering Data Mining SDLC Software engineering Text Mining

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Malicious data detection in IoT using deep learning approach

Malicious data detection in IoT using deep learning approach

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Kolli, Srinivas Srinivasan, Aravindan Manikandan, R. Prasad, Shalini Kumar, Ashok Ramesh, S. Department of Information Technology Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Electronics and Communication City Engineering College Karnataka Bangalore India Department of Computer Science Banasthali Vidyapith Rajasthan India Department of Computational Intelligence Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781032669663

This article compares the efficacy of various DL intrusion detection techniques and identifies the optimal DL methodology for ID in the IoT. In this study, the DL techniques employed were artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent units (GRUs). The suggested model is assessed using a common dataset for IoT intrusion detection. Next, the experimental findings are scrutinized and contrasted with existing IoT techniques for intrusion detection. When compared to the previously used approaches, the suggested technique appeared to have the highest accuracy (99.9%). © 2024 The Author(s).

关键词： Intrusion detection

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Hybrid architecture CNN-BLSTM for automatic speech recognition 3

Hybrid architecture CNN-BLSTM for automatic speech recogniti...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Dar, Muzaffar Ahmad Pushparaj, Jagalingam Vellore Institute of Technology School of Advanced Sciences Department of Mathematics Tamil Nadu Vellore India Vellore Institute of Technology School of Computer Science and Engineering Department of Computational Intelligence Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

With several deep learning approaches, the domain of automatic speech recognition (ASR) has seen notable advancements in recent times. The domains of intelligent human- computer interaction and machine translation greatly benefited from accurate speech recognition. The present study introduces a hybrid architecture amalgamating a convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM) for speech recognition. For aligning speech input sequences with corresponding text output sequences, it uses the connectionist temporal classification (CTC) technique. The experiments were done on the LJ speech dataset, and the results showed with an increased number of training samples, the performance of the speech recognition algorithm tended to be increased but the time taken for training gradually increased over time. Moreover, a trained speech recognition algorithm exhibits a longer training time when the recognition accuracy is lower. In this study, we implemented a hybrid deep learning model CNN-BLSTM, in conjunction with the CTC loss function, which attains a word error rate (WER) of 36.97% on the testing dataset. © 2024 IEEE.

关键词： Speech recognition

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Age Classification through Handwriting Analysis using Convolutional Neural Networks and Pre-Segmented Offline Handwritten Gurumukhi Characters 7

Age Classification through Handwriting Analysis using Convol...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Singla, Chinu Maini, Raman Kumar, Munish Punjabi University Department of Computer Science And Engineering Punjab Patiala India Maharaja Ranjit Singh Punjab Technical University Department of Computational Sciences Punjab Bathinda India

ISBN: (纸本)9798350371406

Handwriting analysis is increasingly gaining prominence across various domains, including forensics, education, and biometrics, owing to its invaluable insights. This article introduces an innovative approach to age classification based on offline handwriting in Gurumukhi script, employing a Convolutional Neural Network (CNN) architecture, specifically the Inception-V3 model. The method utilizes pre-segmented offline Gurumukhi characters, commonly employed in the Punjabi language, as input features. This pre-segmentation process ensures precise and efficient extraction of individual characters from handwriting samples. The CNN model undergoes training on a substantial dataset of annotated Gurumukhi characters, encompassing a diverse range of age groups. The dataset comprises 74,760 Gurumukhi characters contributed by 267 writers, with 137 males and 130 females, each providing 280 characters. By inputting the extracted features into the CNN model, our approach accurately predicts individuals' age groups based on their handwriting samples. Experimental results underscore the efficacy of the proposed method, achieving an impressive accuracy rate of 92.7% in age classification. This high classification accuracy unequivocally validates the potential of our method for age classification in handwriting analysis. It underscores its applicability in forensic investigations and document verification scenarios, highlighting the promising role of handwriting analysis in various fields that necessitate age-related information. © 2023 IEEE.

关键词： Deep neural networks

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Improving Quantum Circuit Synthesis with Machine Learning 4

Improving Quantum Circuit Synthesis with Machine Learning

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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Weiden, Mathias Younis, Ed Kalloor, Justin Kubiatowicz, John Iancu, Costin University of California Department of Electrical Engineering and Computer Science Berkeley United States Computational Research Division Lawrence Berkeley National Laboratory United States

ISBN: (纸本)9798350343236

In the Noisy Intermediate Scale Quantum (NISQ) era, finding implementations of quantum algorithms that mini-mize the number of expensive and error prone multi-qubit gates is vital to ensure computations produce meaningful outputs. Unitary synthesis, the process of finding a quantum circuit that implements some target unitary matrix, is able to solve this problem optimally in many cases. However, current bottom-up unitary synthesis algorithms are limited by their exponentially growing run times. We show how applying machine learning to unitary datasets permits drastic speedups for synthesis algorithms. This paper presents QSeed, a seeded synthesis algorithm that employs a learned model to quickly propose resource efficient circuit implementations of unitaries. QSeed maintains low gate counts and offers a speedup of 3.7 x in synthesis time over the state of the art for a 64 qubit modular exponentiation circuit, a core component in Shor's factoring algorithm. QSeed's performance improvements also generalize to families of circuits not seen during the training process. © 2023 IEEE.

关键词： Machine learning

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Correction: ExAIRFC-GSDC: An Advanced Machine Learning-Based Interpretable Framework for Accurate Gas Leakage Detection and Classification

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International Journal of computational Intelligence Systems 2025年第1期18卷 1-2页

作者： Lalithadevi, B. Krishnaveni, S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India

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Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private States 30

Opinion Mining Using Pre-Trained Large Language Models: Iden...

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Joint 30th International Conference on computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Ahmadnia, Saeed Jordehi, Arash Yousefi Heyran, Mahsa Hosseini Khasheh Mirroshandel, Seyed Abolghasem Rambow, Owen Department of Computer Engineering Department of Linguistics United States Institute for Advanced Computational Science United States Stony Brook University Stony BrookNY United States University of Guilan Guilan Rasht Iran

ISBN: (纸本)9782493814104

Opinion mining is an important task in natural language processing. The MPQA Opinion Corpus is a fine-grained and comprehensive dataset of private states (i.e., the condition of a source who has an attitude which may be directed toward a target) based on context. Although this dataset was released years ago, because of its complex definition of annotations and hard-to-read data format, almost all existing research works have only focused on a small subset of the dataset. In this paper, we present a comprehensive study of the entire MPQA 2.0 dataset. In order to achieve this goal, we first provide a clean version of MPQA 2.0 in a more interpretable format. Then, we propose two novel approaches for opinion mining, establishing new high baselines for future work. We use two pre-trained large language models, BERT and T5, to automatically identify the type, polarity, and intensity of private states expressed in phrases, and we use T5 to detect opinion expressions and their agents (i.e., sources). © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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