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Retraction Note: HQDCNet: Hybrid quantum dilated convolution neural network for detecting covid-19 in the context of big data analytics

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Multimedia Tools and Applications 2024年第38期83卷 86337-86337页

作者： Tenali, Nagamani Rama Mohan Babu, Gatram Department of CSE Y.S. Rajasekhar Reddy University College of Engineering & Technology Acharya Nagarjuna University Guntur India Computer Science and Engineering (AI&ML) RVR & JC College of Engineering Guntur India

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SE-Resnet152 Model: Early Corn Leaf Disease Identification and Classification using Feature Based Transfer Learning Technique

SE-Resnet152 Model: Early Corn Leaf Disease Identification a...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Ponugoti Kalpana Yerrolla Chanti Ravi G Regan D Piyush Kumar Pareek Department of CSE AVN Institute of Engineering & Technology Hyderabad India School of Computer Science & Artificial Intelligence SR University Warangal India Dept of ECE Sona College of Technology Salem India Department of ECE Siddartha Institute of Science and Technology Puttur India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India

Various corn leaf diseases reduce the quantity and quality of corn crop production, so early detection and classification are important for preventing crop yield. However, the detection and classification of corn leaf disease are more difficult due to the regions of the leaf blur and noise effect. To solve the above-mentioned problems, a feature-based transfer learning approach called the Convolutional Deep Learning (CDL) model is proposed. First, the images of corn leaves taken from Plant Village are preprocessed with Green Channel Conversion (GCC) and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms to enhance low-contrast images. Then the important features are extracted through ResNet152, which contains two fully connected layers with a sigmoid function. Next, squeeze and excitation are combined with ResNet152 to gain better performance in detection and classification. The proposed SE-ResNet152 model outperforms with a Precision 98.71%, Recall 98.14%, F1-score 96.87% and accuracy 99.02% respectively compared to existing model like U-Net and ResNet50 models.

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Wireless Capsule Endoscopy Bleeding Images Classification using CNN Based Model

Wireless Capsule Endoscopy Bleeding Images Classification us...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Gotte Ranjith Kumar J. Jeyasudha Ravi G T. Jagadesh Madhura G K School of Computer Science & Artificial Intelligence SR University Warangal India Department of Computational Intelligence SRM Insitute of Science and Technology Chennai India Dept of ECE Sona College of Technology Salem India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India

Wireless Capsule Endoscopy (WCE) is an effective utensil with probe alimentary region diseases and executes effortless visualizing in intestine. Numerous enterprises produce its broad connection and adaptation challenging things comparable efficacity, forbearance, immunity, and consummation. Self-regulated study of the WCE handed datafile is of immense significance for discriminating peculiarity. Detecting a case’s gastrointestinal region through WCE outcomes a huge datafile that involves a significant quantum moment with an exceptional competence put from the therapeutic knowledge. So many computers-backed, eyesight-grounded results can be recommended to determine this controversy, so far it don't give asked position in delicacy, farther advancements just the same as demanded. Combine with deep neural network to develop a proposed model called BIR (bleedy image recognizer) to classify images of bleeding detected by a WCE. The BIR model is combination of MobileNet and a custom-built BERT model. BIR utilizes the MobileNet model for original-position calculation for its lesser calculation energy demand and latterly the affair is fed to the BERT model for farther processing. utilized a datafiles consisting of 1650 pictures captured by WCE for the purpose of training and testing the BIR mode.

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Artificial Intelligence Assisted Service Marketing Using Deep Assisted Neural Network

Artificial Intelligence Assisted Service Marketing Using Dee...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Shanthana S Vidhya Gopal Durgaprasad Navulla Sudeshna Seal Mohammed Ali Sohail Gunti Anil Paul Department of CSE (AI & ML) Rajalakshmi Institute of Technology Chennai Tamilnadu India Department of Computer Science and Engineering Sona College of Technology Salem Tamilnadu India Lakshmaiah Education Foundation Vaddeswaram Guntur Andhra Pradesh India OmDayal Group of Institutions Uluberia West Bengal India Department of Computer & Network Engineering College of Computer Science & Information Technology Jazan University Jazan K.S.A Department of Mechanical Engineering Karunya Institute of Technology & Sciences Coimbatore Tamilnadu India

Improving marketing abilities, the quality of one product, and one connections with one target audience are at the service marketing approach. The marketing of goods and services can both be considered to come within the scope of this category. This data is stored in a centralized location in these databases for ease of access and analysis. Based on the findings of this study, ai has played a crucial role in big data analytics, making it possible to foresee and provide guided experiences that are individualized based on the needs of customers. In this paper, we develop a ai assisted customer support system to deliver experiences that result in advocates and consumers that remain loyal over time, the usage of ai and predictive analytics is crucial.

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Smart Plant Leaf Disease Detection System using Internet of Thing (IOT) and PLDP Net-RF Model

Smart Plant Leaf Disease Detection System using Internet of ...

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： R. Santhoshkumar B. Rajalingam P. Deepan P. Santosh Kumar Patra Department of Computer Science and Engineering St. Martins Engineering College Secunderabad Department of Artificial Intelligence and Data Science St. Martins Engineering College Secunderabad Department of CSE (AI & ML) St. Martins Engineering College Secunderabad St. Martins Engineering College Secunderabad

The decline in apple yield is largely due to diseases that harm the apple's leaves. As a result, it is critical that diseases of citrus plants be detected using an intelligent detection technology. Many artificial intelligence problems can now be solved using deep learning methodologies. Consequently, we decided to use this technology to identify diseases that can impact citrus fruit and leaves. A model based on convolutional neural networks (CNNs) is proposed in this study utilising an integrated strategy. There was a need to construct a model to discriminate healthy vegetables and plants from those with typical apple leaf diseases like black rot and apple scab, therefore the Random Forest (RF) model was devised. The PLDP Net-RF model, which was introduced, may extract complementary discriminative qualities by merging many different layers of data. A number of cutting-edge deep learning models were tested against the RF model on the PlantVillage datasets. The PLDP Net-RF model outperforms its competitors in a variety of evaluation metrics, according to the results of the tests. For farmers who are concerned in detecting apple leaf diseases, the PLDP Net-RF model is a beneficial tool.

关键词： Radio frequency Deep learning Measurement Image segmentation Neural networks Merging Internet of Things

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Expression of Concern for: Applying Deep Learning Strategies for Optimizing Real-Time Data Analysis in Machine Learning

Expression of Concern for: Applying Deep Learning Strategies...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Varun Mishra Sudhanshu Dev Akhilendra Pratap Singh Gautham Krishna Geetha B Rutuja S. Kadam Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Civil Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798350319125

ISBN: (纸本)9798350319132

Deep mastering is becoming increasingly essential for fact analysis in machine learning. It has been used to remedy various applications, from natural language processing to photograph recognition, and has made goodsized achievements in many fields. In this paper, we discover a way to use deep getting-to-know for actual-time data evaluation optimization in gadget learning. Mainly, we present a deep getting-to-know framework for real-time statistics analysis optimization, including an expansion of deep gaining knowledge of fashions and techniques. We speak about the capability of actual-time statistics evaluation optimization with deep getting-to-know and numerous improvement techniques for making the optimization method extra green. Eventually, we offer an empirical evaluation of the proposed framework in machine studying packages, demonstrating the usefulness of presenting a deep learning-based totally technique to real-time statistics analysis optimization. We finish the paper with a dialogue of destiny directions for deep learning research for actual-time information evaluation optimization.

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Effective Information Capture and Retrieval Through Machine Learning-Based Systems

Effective Information Capture and Retrieval Through Machine ...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Sharmila. P Rutuja S. Kadam Bharat Bhushan Vinay Kumar S B Shweta Singh Inzimam ul Hassan Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Information Technology Pune INDIA Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India

Effective information capture and retrieval is crucial for organizations to meet their business needs and remain competitive in today's fast-paced digital world. Traditional information management systems, while useful, are often limited in their ability to handle large amounts of data and provide accurate and relevant results. This paper proposes the use of machine learning-based systems to enhance the process of information capture and retrieval. These systems leverage the power of artificial intelligence to automatically learn from data patterns and improve their performance over time. This approach has shown promising results in various industries, such as finance, healthcare, and e-commerce. Through the utilization of machine learning algorithms, organizations can achieve more efficient and accurate information capture and retrieval, leading to better decision-making and improved business outcomes. The benefits and challenges associated with using machine learning-based systems for information management are discussed, along with potential future research directions in this field. Overall, this paper highlights the importance of utilizing machine learning-based systems for effective information capture and retrieval and the potential impact they can have on organizational success.

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Early Prediction of Diabetes and its Risk Factors based on ARIMA-ELMAN ANN Network

Early Prediction of Diabetes and its Risk Factors based on A...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： J. Senthil Shaik Akbar Akiladevi N S Praveena Ravindar K Banupriya V Department of Food Technology Paavai Engineering College (Autonomous) Namakkal Tamilnadu India Department of CSE(AI & ML) Geethanjali College of Engineering and Technology Hyderabad India Department of Mathematics Sri Eshwar College of Engineering Kondampatti Coimbatore India Dept of ECE Mahatma Gandhi Institute of Technology Gandipet Hyderabad Telangana India School of Sciences SR University Warangal Telangana India Department of Computer Science and Business Systems M.Kumarasamy College of Engineering Karur India

The prevalence of chronic diabetes is high. If diabetes could be predicted sooner, treatment results could be *** such use of data mining techniques is in the field of disease prediction and early detection. To illustrate the potential value of these traits in predicting the presence or absence of diabetes, it is necessary to first explain the relationship between them. Technologies execute a wide variety of activities connected to diabetes, including clustering, association rule mining, and significant attribute selection determination. Priorities should be data preprocessing, feature selection, and model training. When it comes to data preprocessing, the cleansing and cleansing stage is in charge of dealing with errors, missing values, and inconsistencies. It takes advantage of the efficiency of the clustering algorithm and the reversing operation to exclude features with low F-scores. Prior to training ARIMA-ELMAN-ANN models, feature selection is necessary. The proposed approach clearly outperforms the two front-runners, ANN and ELMAN. Accuracy increased by 96.31 percent when the strategy was put into action.

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Segmentation of Liver Tumors in CT Scans Using Active Contour Models and Machine Learning

Segmentation of Liver Tumors in CT Scans Using Active Contou...

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Communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Rajita Sharma Haider Alabdeli Abhishek Singh Baghel J. Jayashankari G. Maheswari S. Dhanashree Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India Department of AI & ML Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

Liver tumor segmentation in CT scans plays a crucial role in cancer diagnosis and treatment planning. This paper proposes a novel methodology that integrates Active Contour Models (ACMs) with Convolutional Neural Networks (CNNs) for accurate and efficient segmentation of liver tumors. The approach leverages the feature learning capabilities of deep learning and the precise boundary detection of ACMs to achieve state-of-the-art segmentation results. Evaluation on the LiTS dataset demonstrates superior performance in terms of Dice Similarity Coefficient (DSC), Precision, Recall, and computation time. The proposed methodology offers a promising solution for improving clinical decision-making in liver cancer treatment.

关键词： Representation learning Deep learning Liver cancer Computed tomography Computational modeling Liver Planning Convolutional neural networks Tumors Active contours

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A Deep CNN Self-Attention Model for Multidimensional Speech Quality Prediction Using Crowdsourced Datasets

A Deep CNN Self-Attention Model for Multidimensional Speech ...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Annapurna Gummadi Chamandeep Kaur Deepak Gupta Vuda Sreenivasa Rao Santosh Tripurana P. Nagaraj Department of CSE (Data Science) CVR College of Engineering Hyderabad India Computer Science & Information Technology Department Jazan University Jazan Kingdom of Saudi Arabia Department of Mechanical Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram A.P. India Dept of ECE Vignan's Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of AI & ML K. Ramakrishnan College of Engineering Trichy Tamil nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Predicting voice quality is critical for improving communication systems and the user experience. In this article, we offer a unique technique to multidimensional speech quality prediction using a Deep Convolutional Neural Network (CNN) enriched with self-attention techniques and crowdsourced datasets. The proposed model incorporates both spatial and temporal information from speech signals via numerous convolutional layers, enabling it to accurately represent the intricate patterns and features seen in speech data. Furthermore, self-attention methods are used to let the algorithm to focus on relevant parts of the input data, increasing its discriminative capacity and tolerance to interference. Transfer learning is utilized to leverage pre-trained representations from large-scale speech datasets, enabling the model to generalize well to diverse speech characteristics and environments. By fine-tuning the pre-trained model on the specific crowdsourced dataset, we adapt it to the task of speech quality prediction while mitigating the need for large annotated datasets, which are often costly and time-consuming to obtain. Experimental results demonstrate the effectiveness of the proposed approach, achieving state-of-the-art performance on various metrics for multidimensional speech quality prediction tasks. Furthermore, extensive ablation studies are conducted to analyze the contributions of different components of the model, highlighting the importance of both the CNN architecture and self-attention mechanisms. Overall, our findings suggest that the proposed deep CNN self-attention model, combined with transfer learning from crowdsourced datasets, presents a promising approach for accurate and efficient speech quality prediction in diverse real-world scenarios

关键词： Measurement Adaptation models Convolution Computational modeling Transfer learning Interference Predictive models User experience Data models Convolutional neural networks

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