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共 811 条记录，以下是481-490 订阅

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OSOL: Open Linked Data Generation Using Semantics Oriented learning Paradigms 15th

OSOL: Open Linked Data Generation Using Semantics Oriented L...

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15th international conference on soft computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired computing, NaBIC 2023

作者： Aravind Krishnan, A. Deepak, Gerard Santhanavijayan, A. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9783031889912

In the era of Web 3.0, there is an imminent need for a strategic framework for open linked data and meta tag generation for web pages as they would be useful in indexing which would serve as a roadmap for retrieval and also several other applications such as text summarization, text mining, etc. this paper proposes an open-linked data generation framework in which the XGBoost classifier classifies the web URL dataset. Category extraction and URL canonicalization are performed which helps in strategic maximal utilization of the term set. Strategic knowledge base repositories like CYC and NELL are used for entity enrichment in the model. the RBFNN classifies the web-based directories extracted from the current structure of the web using a focused crawler. Unlike most traditional frameworks, the OSOL uses Semantics-oriented reasoning, which is achieved through the SimRank measure with an empirically decided threshold. the Eagle optimization algorithm (a metaheuristic, nature-inspired algorithm) generates the optimal solution set. Shannon’s entropy helps in feature selection for classifying the dataset. this framework can achieve an overall mean precision percentage of 95.17%, a mean recall percentage of 96.75%, and an FDR value of 0.05, making it the best-in-class model for linked open data and meta-tag generation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Knowledge engineering

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Electricity Billing and Consumption Prediction using machine learning

Electricity Billing and Consumption Prediction using Machine...

引用

international conference on computing Communication Control and Automation (ICCUBEA)

作者： Mrunalini Bhandarkar Ashok Suryawanshi Seema Deoghare Nikhil Isaac Ajinkya Hegu Piyush Wankhede Pimpri Chinchwad College of Engineering Pune Pune India

this research suggests a AI-learning based method for forecasting electricity usage and bill amounts. the project develops and assesses a number of machine learning models using historical electricity use data and customer billing information. the dataset, which consists of several months' worth of historical data from a residential power client, is used to train and test the models. the study shows that the suggested machine learning models are highly accurate in forecasting electricity consumption and bill amounts. According to the results, machine learning-based methods can greatly increase the precision of predictions of power use and bills, which can be helpful for both consumers and utility providers. the goal of this paper is to create a system that fuses the findings of various machine learning techniques, in order to conduct accurate prediction for a tenant. K nearest neighbour, Support vector machine and Artificial Neural Networks are few of the algorithms that are employed. Each algorithm's accuracy is determined along with the model's accuracy. the model for predicting is then chosen from those with decent accuracy. the dataset used for building the model is Smart metres Database, which was selected from Kaggle website.

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Predictive Analysis for Retail Shops using machine learning for Maximizing Revenue

Predictive Analysis for Retail Shops using Machine Learning ...

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international conference on ent computing and Control Systems (ICICCS)

作者： Aman Malik Gauri Dargar Arti Sharma Priyambada Pandey Computer Science KIET Group of Institutions Ghaziabad India

the issue of difficulties in controlling the inventory of small retailers with limited space and limited capital to buy just a few merchandises has just been led due to the continuous increase in the variety of products. In order to successfully manage the inventory of small stores, this article intends to investigate several machine learning algorithms for forecasting future product demand. the quality of the forecasts that are offered has a significant impact on the effectiveness of procedures not only in retail locations but also throughout the supply chain they are a part of Nowadays, there is a large amount of information that can be cleansed and used for forecasting by utilizing the right machine learning algorithms. this prediction system will help small retail shop owners in maximizing their profit margins. Along with sales forecasting, this study intends to perform customer segmentation to make effective decisions in order to generate a good amount of revenue.

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Optimising Face Recognition System using Contrastive learning and Contrastive Loss

Optimising Face Recognition System using Contrastive Learnin...

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international conference on computing Methodologies and Communication (ICCMC)

作者： Allamsetty Jayanth Kumar Ayinampudi Manvitha Bondalapati Poojitha Meka Mahitha K. Ashesh K V D Kiran Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP India

the current issue with facial recognition systems is that they can be influenced by poor illumination or inadequate image quality. Because camera angles are obstructed, the data may not match the person's nodal point. there are many more difficulties, such as variations in facial appearances such as illumination, pose, expression, and aging. To tackle this difficulty, this study has developed a new strategy by utilizing contrastive learning for face recognition system. Contrastive learning is a machine learning (ML) approach that integrates information from two faces to understand the unique qualities of an individual's face. Contrastive learning improves the system's vision by generating two vectors, positive and negative, by varying the image contrast. the model is then trained to learn about images and recognize them more efficiently and effectively by establishing those two vectors.

关键词： Training Image recognition Face recognition Computational modeling machine vision Lighting machine learning

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Spam Email Filtering using machine learning Algorithm

Spam Email Filtering using Machine Learning Algorithm

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international conference on computing Methodologies and Communication (ICCMC)

作者： Dinesh Komarasamy Oviya Duraisamy Mohana Saranya S Sandhiya Krishnamoorthy SanjeevKumar Rajendran Dharani M K Computer Science and Engineering Kongu Engineering College Erode India

Email is one of the most used modes of communication by many industries and IT sectors. Even common people used to communicate through email about business related in-formation over the internet As technology grows, the threat to the individual has also been increased. In the Email system, the threat takes the form of spam email. there are several existing spam filtering methods currently in use including knowledge-based techniques, learning-based techniques, clustering methods, and so on. the proposed work provides an overview of several existing methods that use machine learning techniques such as Naive Bayes, Support Vector machine, Random Forest, Neural Network and formulated new model with improved accuracy. However, in this work, the discussion and consolidated analysis has been done by comparing several email spam filtering techniques.

关键词： Support vector machines Industries machine learning algorithms Filtering Unsolicited e-mail Neural networks Knowledge based systems

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Artificial Intelligence, machine learning, and Natural Language Processing Capabilities in Modern Business Intelligence

Artificial Intelligence, Machine Learning, and Natural Langu...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Prakash Ukhalkar Manasi Bhate Suraj Hingane MCA Department Pimpri Chinchwad College of Engineering Pune India MCA Department MES's Institute of Management and Career Courses Pune India MCA Department Abhinav Education Society's Institute of Management and Research Pune India

Operational business intelligence capabilities in modern business applications demand the simultaneous processing of analytical queries and business transactions using the same data. Mixed workloads become the result of this. the business sector is being disrupted by artificial intelligence, machine learning, and natural language processing. the voice-based search, high-tech and telecommunications, automotive and assembly, and financial services industries are just a few of the sectors setting the pace for adopting these particular technologies. this study demonstrates that real-time analysis, machine learning, and natural language processing are essential for current business intelligence systems to make well-informed decisions and provide company value across various industries.

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Analyzing Data Scientist Salaries Dataset through machine learning Algorithms Using Tool “Orange”

Analyzing Data Scientist Salaries Dataset Through Machine Le...

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international conference on computing Communication Control and Automation (ICCUBEA)

作者： Shikha Mittal Chetna Monga Ankit Bansal Neeraj Singla Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

the buzz surrounding artificial intelligence has made machine learning a hot topic right now. Although there are many tools for visualising data, scripting languages are primarily used for model training. the Orange Data Mining tool gives you a lot of ways to change how data is preprocessed, how it is displayed, how models are trained, and how models are tested. In order to establish which strategy has the best Classification Accuracy and Precision, the proposed research uses machine learning techniques to predict the size of an organization based on a variety of parameters, including employee experience, income, job type, employee type, etc. the effectiveness of various machine learning techniques, including Naive Bayes, random forests, support vector machines, neural network, logistic regression, was evaluated. Classification Accuracy evaluations are performed by cross validation. For this paper, we consulted the Kaggle dataset.

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Non-Invasive Mental Health Prediction using machine learning: An Exploration of Algorithms and Accuracy

Non-Invasive Mental Health Prediction using Machine Learning...

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international conference on ent computing and Control Systems (ICICCS)

作者： Vanshika Bajaj Rishabh Bathija Chandni Megnani Jasmine Sawara Nusrat Ansari Computer Engineering Department V.E.S. Institute of Technology Mumbai India

the potential of the machine learning in predicting mental health outcomes is investigated in this study. Two datasets were gathered: one of mental health patient questionnaires and the other of information from MRI scans of Alzheimer’s patients. the datasets were pre-processed using techniques such as stop word removal and lemmatization, and the processed data was encoded for increased prediction accuracy. To find the highest performing model, various algorithms such as Logistic Regression, Decision Tree, KNN (K-Nearest Neighbors), Adaboost, Random Forest, and Logistic Regression are examined. the findings indicated that machine learning algorithms can predict mental health outcomes with high accuracy, and that adding demographic, behavioural, and psychological factors can improve prediction accuracy even more. the study emphasizes the significance of creating accessible and accurate mental health prediction tools, as well as the promise of the machine learning in mental health evaluation.

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VisionX—A Virtual Assistant for the Visually Impaired Using Deep learning Models 7th

VisionX—A Virtual Assistant for the Visually Impaired Using...

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7th international conference on Emerging Research in computing, Information, Communication and Applications, ERCICA 2022

作者： Royal, Akula Bhargav Sandeep, Balimidi Guru Das, Bandi Mokshith Bharath Raj Nayaka, A.M. Joshi, Sujata Nitte Meenakshi Institute of Technology Bangalore560064 India

ISBN: (纸本)9789811954818

We are living in world where there are many difficulties faced by various people. But, visual impairment is one of the biggest challenges faced by people. We can see visually impaired people using sticks or any other means for doing their tasks, but they can’t find or identify any object by themselves. In today’s world, there are many advancements in technology like artificial intelligence, machine learning where we can train machines for identifying different objects and different text present in a scene. the objective of this work is to develop a system using Esp32 Camera module to help the visually impaired to identify objects. the proposed system takes images from Esp32 and identifies different objects and text present in the scene and gives the output in the form of speech. the system has a voice assistant with commands for performing object detection, text detection and camera usage. this research helps visually impaired people in various aspects like object detection and text detection using voice commands and they can listen to the description of different objects detected by the system. the results show training accuracy of 97% and test accuracy of 88% using deep learning CNN models. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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machine learning for Drug Discovery Using Agglomerative Hierarchical Clustering 1

引用

5th international conference on soft computing and Signal Processing, ICSCSP 2022

作者： Lakshmi, B. S. S. Sowjanya P, Ravi Kiran Varma Maharaj Vijayaram Gajapathi Raj College of Engineering AP Vizianagaram India

ISBN: (数字)9789811986697

ISBN: (纸本)9789811986680

Mining of chemical information from large databases of chemical compounds leads to drug discovery. Both supervised and unsupervised algorithms when applied to the databases increase the efficacy of identifying a new target and optimize the lead compound. machine learning (ML) can be applied to identify the drug target and in optimization of lead compound. ML techniques are proved to reduce the failure rate when compared to traditional methods. this paper mainly works on compound similarity prediction using clustering. Some of the clustering algorithms are k-means, agglomerative hierarchical, fuzzy c-means. through this compound similarity prediction, the compounds are clustered according to their similarity. So, if the reaction of a new compound is unknown, we can know its reaction through this compound similarity prediction. the primary benefit of compound similarity prediction is that it allows one to predict the reaction of a new compound. We can speed up the drug discovery process if we know how a novel molecule reacts. Agglomerative hierarchical clustering gives best result because the value of Davies–Bouldin index is minimum when compared to k-means, fuzzy c-means clustering algorithm. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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