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A Deep Neural Network Based Holistic Approach for Optical Character Recognition of Handwritten Documents

SN computer science 2024年第4期5卷 347页

作者： Mukherjee, Jayati Department of Computer Science and Engineering Academy of Technology West Bengal Adisaptagram 712121 India

Optical Character Recognition of handwritten document has been a research topic for last few decades now. Different type of classification schemes starting from template matching, structural analysis to deep neural network have been proposed by researchers. In this paper, a novel holistic approach is proposed for recognition of handwritten words. The approach is a hybrid model combining CNN and BLSTM layers which are responsible for extraction of spatial and temporal features respectively from the word images. Both the features are combined by compact bi-liner pooling. The CBP layer highlights the fine grained details which in turn help to achieve high recognition accuracy. The extracted feature is recognized by a connectionist temporal classification layer. The weights are learnt based on the database using the back propagation algorithm. The hybrid model is trained using three public databases CMATERdb2.1.2, IIIT-HW-Dev and IIIT-HW-Telugu containing Bengali, Devanagari and Telegu handwritten words respectively. The proposed model has achieved 96.42%, 94.79%, 95.07% accuracy on the Bengali, Devanagari and Telegu databases respectively. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： computer vision Document analysis Handwriting recognition Image analysis Optical character recognition Pattern recognition

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Real Time Fire Detection and Alert Triggering System

Real Time Fire Detection and Alert Triggering System

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Balakrishnan, D. Mariappan, Umasree Kumar, Netapalli Nithish Nagaprudhvi, Kothuru Premchand, Muthyam Vijay, Gopala Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnakoil India Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Virudhunagar India

ISBN: (纸本)9798350315455

The real-time fire detection and triggering system presented in this study harnesses camera images for swift and reliable fire detection, leveraging a multi-stage approach. The system's preprocessing phase involves image enhancement and noise reduction to optimize image quality, while the feature extraction step extracts relevant information from the images to improve discrimination between fire and non-fire regions. Classification is carried out using a machine learning model, like Convolutional Neural Networks (CNNs), to discern potential fire events in the images. Upon detecting a fire, the system triggers immediate alarms or safety protocols, thus enhancing fire prevention and response mechanisms in real-time scenarios. The overall implementation of the research work is done in the python environment from which it is proved that the proposed methodology attains better performance than the existing research methodology. The proposed work attains better performance than the existing research methodologies in terms of 7% increased accuracy. And in terms of precision, recall and f-measure proposed method attains 6%, 4% improvement than the existing research methodologies. © 2023 IEEE.

关键词： alert triggering system convolutional neural network feature extraction Fire detection image quality

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Alzheimer Disease Prediction Using Recursive Feature Elimination and Artificial Neural Network

Alzheimer Disease Prediction Using Recursive Feature Elimina...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Balakrishnan, D. Mariappan, Umasree Sai Prudhvi, Bodempudi Saikumar, Patha Mohan Mahesh, Boggavarapu Mani Sai, Polisetty Kalasalingam Academy of Research and Education Department of Computer Science and Engineering TamilNadu Krishnankoil626126 India Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering TamilNadu Virudhunagar626005 India

ISBN: (纸本)9798350300826

Alzheimer's Disease (AD) is a devastating neurodegenerative disorder that affects a significant portion of the aging population worldwide. Early prediction and detection of Alzheimer's disease are crucial for providing timely interventions, personalized treatment plans, and improved patient care. In recent years, machine learning techniques have shown promise in aiding the prediction of Alzheimer's disease by leveraging the analysis of diverse data sources, including demographic information, genetic markers, biomarkers, neuroimaging data, and cognitive assessments. In this paper, a combined technique called Recursive feature elimination with Artificial Neural Network (R-ANN) is introduced for fast processing and better prediction of AD. In the combined technique, the Recursive Feature Elimination (RFE) selects the most important features in the dataset for reducing the computational complexity and improves the disease prediction accuracy. RFE can handle non-linear relationships between features and the target variable by utilizing machine learning models capable of capturing complex interactions. This allows RFE to potentially capture non-linear dependencies between features and the disease being predicted. The selected features are used in ANN for prediction of AD. The empirical results prove that the accuracy, precision, recall and F-measure of proposed R-ANN is 4.71%, 4.6%, 3.41%and 3.37% better than Regularized Extreme Learning Machine (RELM)technique respectively for AD prediction. © 2023 IEEE.

关键词： Forecasting

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Multiattribute Deep CNN-Based Approach for Detecting Medicinal Plants and Their Use for Skin Diseases

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 710-724页

作者： Dalvi, Prachi Kalbande, Dhananjay R. Rathod, Surendra Singh Dalvi, Harshal Agarwal, Amey Bharatiya Vidya Bhavans Sardar Patel Institute of Technology Department of Computer Engineering Mumbai400058 India Fr. Conceicao Rodrigues College of Engineering Department of Electronics and Computer Science Mumbai400050 India Dwarkadas J Sanghvi College of Engineering Department of Information Technology Mumbai400056 India

Skin health is a critical concern for humans, especially in geographical areas where environmental conditions and lifestyle factors adversely affect their condition, leading to a prevalence of skin diseases. This issue is exacerbated in rural regions, like parts of India, where a notable dermatologist shortage exists, leading to overlooked skin diseases. In response, the use of medicinal plants for dermatological purposes has been a longstanding tradition. However, traditional plant identification often relies on a single attribute, such as leaves or flowers, which can be unreliable due to seasonal variations. This article introduces a novel approach for accurately identifying medicinal plants using a multiattribute deep convolutional neural network. This approach aims to bridge the gap in healthcare access by empowering individuals to recognize and utilize these plants effectively. Our objective is to develop a robust deep CNN model trained on a diverse dataset of images encompassing leaves, trunks, and seeds of medicinal plants associated with skin health. Our findings demonstrate that the model achieves high accuracy in plant identification, effectively addressing the limitations of single-attribute methods. This research not only contributes to the field of medicinal plant classification but also empowers individuals to make informed decisions about their skin health while preserving valuable traditional knowledge. © 2024 IEEE.

关键词： Medicinal chemistry

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Medical Knowledge Graph:Data Sources,Construction,Reasoning,and Applications

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Big Data Mining and Analytics 2023年第2期6卷 201-217页

作者： Xuehong Wu Junwen Duan Yi Pan Min Li Computer Science and Engineering Central South UniversityChangsha 410083China School of Computer Science Hunan First Normal UniversityChangsha 410006China School of Computer Science and Engineering Central South UniversityChangsha 410083China Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China

Medical knowledge graphs(MKGs)are the basis for intelligent health care,and they have been in use in a variety of intelligent medical ***,understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical *** this end,we offer an in-depth review of MKG in this *** research begins with the examination of four types of medical information sources,knowledge graph creation methodologies,and six major themes for MKG ***,three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path(RIP)is proposed as a means of expressing the reasoning procedures for *** addition,we explore intelligent medical applications based on RIP and MKG and classify them into nine major ***,we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.

关键词： medical knowledge graph knowledge graph construction knowledge reasoning intelligent medical applications intelligent healthcare

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Prediction of Heart Disease Using Hybrid Naïve Bayes Technique 22

Prediction of Heart Disease Using Hybrid Naïve Bayes Techni...

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22nd International Symposium on Communications and Information Technologies, ISCIT 2023

作者： Patil, Shantakumar Lutimath, Nagaraj M. Jogish, D. Premjyoti Patil, Bhargav S. Sai Vidya Institute of Technology Computer Science and Engineering Bengaluru India Dayananda Sagar Academy of Technology and Management Computer Science and Engineering Bengaluru India Bengaluru India B.M.S. College of Engineering Computer Science and Engineering Karnataka Bengaluru560019 India

ISBN: (纸本)9781665457316

The domain of medical diagnosis has attracted many researchers. Several cases of human early mortality have been predicted by investigating the diseases. Different reasons are the cause of several diseases and one such cause is heart disease. Set of disorders in the heart lead to heart disease. It consists of blood vessel issues such as irregular heart beat problems, weak heart muscle problems, congenital heart difficulties, cardio vascular defects and coronary artery disorder. Many researchers have summarized idiosyncratic procedures to preserve human life and help health care experts to recognize, prevent and manage heart disorder. Machine Learning is utilized for decision making in many domains. Naive Bayes technique is one approach which uses conditional probability. In this paper we have taken UCI machine learning data repository including of patients affected from heart disease is analyzed using Complement Naive Bayes probability methodology along with correlated features of the heart disease data set. The prediction is done using this concept as hybrid Naive Bayes probability technique. The advantage of Complement Naive Bayes probability prediction is that, it can be used for imbalanced data set. Accuracy of classification of patients suffering from heart disease is predicted. Implementation is done in Python environment. © 2023 IEEE.

关键词： Python

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Smartphone based human activity recognition irrespective of usage behavior using deep learning technique

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International Journal of Information technology (Singapore) 2025年第1期17卷 69-85页

作者： Kundu, Soumya Mallik, Manjarini Saha, Jayita Chowdhury, Chandreyee Department of Computer Science and Engineering Jadavpur University Kolkata India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Human activity recognition (HAR) from sensory data is a crucial task for a wide variety of applications. The in-built inertial sensor facilities of commercial smartphones have made the data collection process easier. However, different smartphone configurations exhibit variations in sensor readings for the same activities. Different smartphone holding positions, like in hand, shirt, or trouser pockets, also lead to variations in signal patterns for the same activity. Some recent works have shown that automated feature extraction using deep learning methods can significantly improve activity recognition, although there is a lack of experimentation considering device heterogeneity and different smartphone holding positions. The proposed work addresses this research gap with a two-fold contribution. First, a CNN-based HAR framework is proposed that forms 2-D frequency domain images to capture temporal patterns in the data along with inter-axis spatial features. Second, an ensemble of conditional classifiers has been designed based on CNN that exhibits generality in terms of device configurations and usage behavior. Real life data have been collected for different activities using different devices for experimentation. The proposed ensemble model is found to recognize activities with 94% accuracy even when the training and test devices are different for real datasets. © The Author(s) 2024.

关键词： Activity image CNN Deep learning Human activity recognition Smartphone

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BCredS: Blockchain Leveraged Secure System for Credential Management in Emergency Scenarios 4

BCredS: Blockchain Leveraged Secure System for Credential Ma...

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4th International Conference on computer, Communication, Control and Information technology, C3IT 2024

作者： Das, Nabanita Das, Subhashis Academy of Technology Department of Computer Science and Engineering West Bengal India

ISBN: (纸本)9798350389128

Credential management for emergency scenarios is vital for security, access control, accountability, and ensuring the effectiveness of response and recovery efforts while adhering to regulatory requirements. In the aftermath of a disaster or any emergency scenario, control stations/regulatory authorities access several response and recovery systems for providing services. Unauthorized access to these systems creates cyber-attacks. Also, the existing credentials management systems suffer from time-consuming procedures due to the involvement of numerous stakeholders and restricted information access, leading to increased administrative burden, and loss of trust and reputation in the system. As a result, several researchers and emergency response organizations have advised to use of blockchain technology which provides a secure, transparent, and tamper-proof ledger of credentials and access logs. This immutability ensures that no unauthorized changes or deletions occur, enhancing the trustworthiness of the credential data. Hence, this work exploits the concept of blockchain to enrich the credential management system by streamlining the validation mechanism and upgrading security mechanisms. This work develops BCredS, a blockchain-leveraged secure and intelligent credential management system by designing smart contracts that are resistant to hacking, and by utilizing blockchain, the security of the application will be improved since it employs strong cryptographic techniques. The efficiency of the proposed work will be evaluated through extensive simulation in Ethereum platform. © 2024 IEEE.

关键词： Ethereum

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Sentimental Analysis-Based Recommended System for Products Using Machine Learning 8th

Sentimental Analysis-Based Recommended System for Products U...

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Mamatha Bai, B.G. Likhith, S.R. Itagi, Salma Department of Computer Science Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Department of School of Computer Science and Engineering and Information Science Bio-Intelligence Lab Presidency University Karnataka Bengaluru India Department of Computer Science Engineering Sai Vidya Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9789819976218

Sentiment Analysis is widely used in the process of mining the data, to predict emotion of a sentence through Natural Language Processing (NLP). The main aim is to find the accurate polarity of a sentence. Therefore, to find the polarity or sentiment of a user or customer for a product there is a need for automated data analysis techniques. In this paper, a detailed analysis of classification techniques is used in Sentimental Analysis of Amazon Product Reviews with recommendation for a best buy product on web. Multinomial Naïve Bayes, Random Forest, Logistic Regression, Decision Tree, and SVM classifiers are tested and compared. Random Forest gives the best accuracy of 94.94%. Web scraping extracts five Amazon products on *** and recommends the best buy product on the basis of polarity score of each product and here Samsung Galaxy M01 is recommended as the best buy product. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification Machine learning Polarity Sentimental analysis Web scraping

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Smart automated highway lighting system using IoT: a survey

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Energy Informatics 2024年第1期7卷 76页

作者： Achar, Tejaswini Eshwar Rekha, C. Shreyas, J. Department of Computer Science and Engineering Government Engineering College Koppal Talakal India Department of Information Science and Engineering Bapuji Institute of Engineering and Technology Davanagere India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

Efficient highway lighting is crucial for ensuring road safety and reducing energy consumption and costs. Traditional highway lighting systems rely on timers or simple photosensors, leading to inefficient operation by illuminating lights when not needed or failing to adjust to changing conditions. The emergence of the Internet of Things (IoT) and related technologies has enabled the development of smart automated highway lighting systems that can dynamically control illumination levels based on real-time data. This paper provides a comprehensive review of the current state-of-the-art in smart automated highway lighting systems employing IoT technologies. Key components, communication protocols, data processing techniques, and lighting control strategies are discussed. The integration of renewable energy sources and energy storage systems is explored for environmentally sustainable operations. Practical implementation case studies are analyzed to highlight benefits and challenges. Open research issues and future directions for further enhancements are identified. © The Author(s) 2024.

关键词： Street lighting

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