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Analyzing Online Purchase Behaviour Through Machine learning Approach Based Ordering Mechanism

Analyzing Online Purchase Behaviour Through Machine Learning...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Rutvi S. Sheth Chirag S. Thaker Computer Engineering Department L. D. College of Engineering Ahmedabad India

In recent years, the rise of user-generated content has significantly influenced consumer behavior in online shopping. The analysis of product-related data, particularly sentiments expressed by consumers on e-commerce platforms, has garnered considerable attention. It is crucial to examine questions related to various aspects to assist sellers in making informed decisions and maximizing their profits. While it is feasible to sort purchasers into pre-buy or post-buy stages in light of procurement data, there are situations where purchasers might have gained the item from an outside source or on the other hand assuming the buy was made by another relative. To tackle this challenge, a novel text classification method has been proposed in this study that employs an ordering mechanism to determine whether a text was posted before purchase (pre-purchase) or after purchase (post-purchase). The approach presented in this study combines the strengths of transfer learning and sequential learning techniques within a hybrid deep learning model. This integration leads to improved performance of the proposed model, BBRCA, when compared to other machine learning models, as evidenced by higher accuracy, precision, recall, and F1-score.

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Intelligent Application of Voice Recognition and Personal Secretariat for Effective Business Management

Intelligent Application of Voice Recognition and Personal Se...

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2022 Sardar Patel International Conference on Industry 4.0 - Nascent Technologies and Sustainability for 'Make in India' Initiative, SPICON 2022

作者： Vichare, Sumedh Mehta, Varun Tripathi, Raveena Mangrulkar, Ramchandra Kumhar, Sunil D.J. Sanghvi College of Engineering Department of Computer Engineering Maharashtra Mumbai India

ISBN: (纸本)9781665465397

The focus of this work is to design a product catering to small business owners across India. In the current scenario, the task of managing day to day business activities such as handling inventory, sales, employee salary, is carried out manually. This approach is unsystematic, time consuming and it doesn't provide any meaningful insights into the business. It is also prone to errors and data could be lost in the process. The proposed mobile application has been designed to overcome these shortcomings by employing a voice command feature to carry out all the store operations. Additional features include managing staff payments, staff attendance, low inventory alerts and customized product recommendations. Finally, the data is analysed to provide worthwhile information about profits, revenue and sales trends. © 2022 IEEE.

关键词： Collaborative filtering

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Modified Penguin Search Algorithm to Improve Feature Selection in Wireless Sensor Network 2

Modified Penguin Search Algorithm to Improve Feature Selecti...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Jabakumar, A. Kingsly Balasubramaniam, Anuradha Suganthi, B. Balasaranya, K. Christ the King Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Rvs College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350347579

A feature selection approach focuses on lowering the features' dimension while retaining or improving the classifier's predicted accuracy. The importance of the feature selection algorithm increases, particularly in data sets with many variables and features. It will get rid of pointless variables and boost classification performance and accuracy. This article describes the Modified Penguin search algorithm to improve wireless sensor networks' feature selection (MPSA). It handles the forest environment features like temperature, humidity, light intensity, and carbon dioxide in various climatic zones during the morning, afternoon, and night to detect the fire. The objective of the MPSA mechanism is to remove unnecessary features and enhance classification accuracy. The Modified Penguin search algorithm selects the important feature for classification. The proposed system uses the Support Vector Machine (SVM) algorithm to improve the classification performance. The experimental outcomes demonstrate the MPSA is more efficient than the baseline mechanism. The simulation results demonstrate that the MPSA mechanism improves the feature selection accuracy and minimizes the processing time. © 2023 IEEE.

关键词： Support vector machines

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Querylizer: An Interactive Platform for database design and Text to SQl Conversion

Querylizer: An Interactive Platform for Database Design and ...

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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： deshpande, Anushka Kothari, dhruv Salvi, Akash Mane, Prajakta Kolhe, Vaishali D. Y. Patil College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9781665425773

Every day, the world becomes more data-driven. Also, an enormous amount of data generated is managed by RdBMS in the form of Tables and SQl is the language that is used to manipulate it and perform the desired operations. Not all individuals have the required knowledge of SQl language to work on the databases. This paper proposes a system that will convert natural language statements into SQl queries. Natural language Processing, which is a component of AI, will be used by the system. NlP gives the ability to a computer program to understand human language in both spoken and written form. Moreover, the visualization of any system makes it easier to comprehend. Focusing on the low-Code revolution presently in the industry, a visualization software for database management is proposed. © 2022 IEEE.

关键词： Visualization

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Enhanced Multiclass Brain Tumor detection in MRI Using Similarity-Navigated Graph Neural Networks with Banyan Tree Growth Optimization 4

Enhanced Multiclass Brain Tumor Detection in MRI Using Simil...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Babu Rao, K. Singh, Pallavi Verma, Gargi Shankar Kannan, l. Mohana Kadam, Swaraj Mangalore Institute of Technology & Engineering Department of Computer Science and Engineering Karnataka Mijar India Saraswati Dental College & Hospital Department of Public Health Dentistry Uttar Pradesh Lucknow India Krishna's Vikash Institute of Technology Department of Computer Science and Engineering Chhattisgarh Raipur India Erode Sengunthar Engineering College Department of Biomedical Engineering Tamil Nadu Perundurai India Dr. D Y Patil Institute of Technology Department of Electrical Engineering Maharashtra Pune India

ISBN: (纸本)9798331529635

The diagnosis of brain tumors is admittedly difficult because of their varied and complicated nature. Therefore, one has to get the right diagnosis and categorization for it to be treated well. This is a situation where identification of multiple classes such as;meningioma, pituitary, glioma and no tumor is crucial in taking the right course of action. Therefore, a new deep learning architecture is suggested in this work to increase the specificity between different types of tumors by detecting brain tumors with high accuracy in MRI scans. The proposed methodology starts with the Cumulative Curve Fitting Approximation (CCFA) preprocessing algorithm to improve the quality of MRI images. Preprocessed images are used as inputs for feature extraction based on geometric algebra transformers. These transformers include geometric relationships within the data, ensuring more distinctive selection. The categorization is further carried out with assistance of a Similarity-Navigated Graph Neural Network (SNGNN) incorporating the Banyan Tree Growth Optimizer (BTGO) model, which is constructed for efficient detection of the subtlest patterns and their connection between classes of brain tumors. It integrates very well in terms of the accuracy shown by its performance metrics. It shows effectiveness in a multiclass scenario of detecting brain tumors, which helps in the precise and early diagnosis and even tailors proper treatment plans. © 2024 IEEE.

关键词： Graph neural networks

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driver drowsiness Estimation Based on Hybrid Feature Extraction and light weighted dense Convolutional Network

Driver Drowsiness Estimation Based on Hybrid Feature Extract...

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2022 IEEE International Conference on distributed Computing and Electrical Circuits and Electronics, ICdCECE 2022

作者： Sharanabasappa Nandyal, Suvarna Sharnbasva University Department of Computer Science & Engineering Kalaburagi India P D A College of Engineering Department of Computer Science & Engineering Kalaburagi India

ISBN: (纸本)9781665483162

Researchers propose a fully automated method of detecting drowsiness using driving images with a focus on fatigue driving detection. Kanade - lucas - Tomasi - ViolaJones (KlT-ViolaJones) is used to locate feature points and detect faces in the proposed algorithm and feature points are used to extract the region of interest (ROI). In order to determine the status of the eye from the ROI images, Histogram oriented Gradient (HoG) is used. Two parameters with which fatigue can be detected are percentage of eyelid closure over pupil over time (PERClOS) ratio and Eyes Aspect Ratios (EAR). Experimental results demonstrate that the proposed light Weighted dense Convolution Network (li-denseNet) can detect drowsiness levels in drivers using the National Tsing Hua University driver drowsiness detection dataset (NTHU-ddd). The proposed algorithm li-denseNet outperforms other CNN-based methods, AlexNet, VGG, RNN, and ResNet showing accuracy, sensitivity, specificity, precision and F1-Score rates of 98.44%, 91.5%,92.3%,98.2 and 97.02%, respectively. © 2022 IEEE.

关键词： Aspect ratio

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Machine learning Based Smart Assistive device for differently Abled People-SAddAP 4

Machine Learning Based Smart Assistive Device for Differentl...

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4th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2022

作者： Agarkhed, Jayashree Tahreem, lubna P.D.A College of Engineering Department of Computer Science Engineering Karnataka Kalaburagi India

ISBN: (纸本)9781665402392

Recently, Human Activity Recognition (HAR) has turned into an energetic exploration region, particularly because of the spread of electronic gadgets, for example, cell phones, digital watches and camcorders present in our everyday lives. likewise fall accidents are one of the primary sources of passing's because of cracks brought about by clash with the ground or hard obstructions. The failure to see objects around oneself is the most tragic things that could happen to an individual. This likewise annihilates the capacity of the individual to move around without anyone else in known regions and conditions. Over the range of the last decade, a few sorts of gadgets have been intended to help the differently abled individuals to move around in various types of conditions be that as it may, in any case need to have every one of the components for diversely abled individuals or senior's in one gadget. The current paper is the augmentation of our past work to add to say research with a way for offering help to differently abled or elder's people. The proposed smart assistive activity recognition device for differently abled people that perceive the movement of environment around them and assist them with living. It likewise recognizes snags and sound on their route to caution them so they don't get into an accident. The current paper adds to help differently abled individuals or elder's by permitting them to realize the encompassing objects and safe them from getting into any mishap by fall detection and obstacle detection. Activity discovery is turning into an indispensable piece of numerous portable applications. The proposed system utilizes the procedures of machine learning to make an application that would help the differently abled individuals or elders and give them a freshly discovered expectation for cherishing the lavishness and difficulty of the world, meanwhile effectively explore in it. © 2022 IEEE.

关键词： Fall detection

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Simplifying Privacy Agreements using Machine Reading Comprehension and Open domain 6

Simplifying Privacy Agreements using Machine Reading Compreh...

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6th International Conference on Computing, Communication, Control and Automation, ICCUBEA 2022

作者： Sanghavi, Preet Ghamsani, Raj Parekh, Rishi Mota, Ritik dongre, deepika D.J. Sanghvi College of Engineering Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9781665484510

Privacy Agreements are critically important from the company as well as the user's point of view. Upon surveying, we realise there are three most common issues faced by the user while trying to read their privacy agreements. First is excessive use of complex language and jargon that make it extremely difficult for the user to comprehend the agreement. The second issue is that if the user has a few questions with respect to the agreement, they are not answered directly. lastly, there lies an absence of an agreement evaluator, that can help compare the document with the standards of the industry. We aim to alleviate this situation by proposing a three-fold technique involving Text Summarization, automated question answering and a document Evaluator. Reading comprehension is one of the most critical abilities in human intelligence, and machine reading comprehension is one of the most popular research directions in the field of natural language processing. Machine reading is one of the key aspects to achieving artificial intelligence. The introduction of deep learning and large datasets in the recent few years has encouraged the success of MRC. Additionally, the open domain automatic question answering models have been widely studied in recent years. However, the problem of lack of sufficient data to read information from exists that makes questions out of the context of the text seem difficult to answer. In this study, we propose a novel framework to integrate MRC along with Open domain to answer questions with increasing accuracy. This is achieved by classifying the question and retrieving the answer from multiple sources. Our model should be able to extract answers from discontinuous spans across the passage text. The answer that has the highest confidence associated with it is returned as the final answer. Moreover, the existing passage has been updated based on the questions answered. © 2022 IEEE.

关键词： large dataset

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Comparative Analysis of dynamic data Placement Strategy in distributed Cloud Environment 1st

Comparative Analysis of Dynamic Data Placement Strategy in D...

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1st International Conference on data engineering and Machine Intelligence, ICdEMI 2023

作者： Shankar, B. Prabhu Singh, Uday Kumar Vijayalakshmi Viji, C. Rajkumar, N. Stalin, M. Nachiappan, Balusamy Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Department of Computer Science and Engineering CMR Institute of Technology Karnataka Bangalore India Prologis DenverCO80202 United States Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819776153

In general, the dynamic data optimization technique is used to place the user’s data in a cloud system to reduce the latency, intercommunication traffic, data transfer cost, execution cost, load balancing, and bandwidth utilization. The user data is dispersed and stored in a distributed cloud environment, so it must ensure data availability and reliability in cloud data centers. These data placement techniques involve two steps: first user data identification and second identifying the node to place the data in a geographically distributed cloud. The primary goal of the data placement approach is to find the best or optimal node to store user data in terms of latency and storage costs. This comparative analysis provides various survey data placement algorithms and techniques used to analyze latency delay, execution time, access cost, load balance, server over, bandwidth, and server capacity with different parameters. This study also analyzes the merits and demerits of data placement strategies. This survey helps to concentrate on the dynamic data optimization technique in the distributed or multiple cloud environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2024.

关键词： distributed cloud

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VitiNet: Precision Grape disease Identification with deep learning 3

VitiNet: Precision Grape Disease Identification with Deep Le...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Rajan, K. Palanivel Raj, J. Relin Francis Krishnan, R. Santhana Shankar, S. Vijay Kumar, V. Vinoth Kavitha, S. Department of Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electrical and Electronics Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331532420

Grape diseases pose a critical threat to vineyards worldwide, affecting both yield and crop quality. To address this, VitiNet, a deep learning-based algorithm utilizing EfficientNetB7, is designed for precise and scalable grape disease identification. The model focuses on five major grapevine diseases: Powdery Mildew, downy Mildew, Anthracnose, Black Rot, and Esca. Two custom datasets, GrapeVine dataset dataset and VineHealthdataset, were curated to provide a diverse set of images for model training and evaluation. EfficientNetB7, known for its scalability and high performance in image classification, was fine-tuned by unfreezing the last five layers to allow for the extraction of disease-specific features. This fine-tuning enables the model to adapt to the complexities of grape disease identification while maintaining computational efficiency. The model’s implementation includes data augmentation techniques to enhance the diversity of the training set, while Stochastic Gradient descent (SGd) and Categorical Cross-Entropy optimize the model’s learning process. While accuracy, precision, recall, and F1 score were evaluated on both datasets, the core strength of VitiNet lies in its ability to generalize well across varying environmental conditions and disease manifestations. VitiNet presents a significant advancement in precision viticulture, offering a reliable and automated solution for real-time disease monitoring. By enabling early and accurate disease identification, this model supports proactive disease management, ultimately improving vineyard productivity and sustainability. © 2024 IEEE.

关键词： diagnosis

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