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Voice command-integrated AR-based E-commerce Application for Automobiles

Voice command-integrated AR-based E-commerce Application for...

2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Krishnamurthy, Vallidevi Jafrin Rosary, B. Oliver Joel, G. Balasubramanian, Surendiran Kumari, Sakshi Vellore Institute of Technology School of Computer Science and Engineering Chennai India Vellore Institute of Technology School of Mechanical Engineering Chennai India National Institute of Technology Puducherry Department of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350312126

This research work aims to create an Augmented Reality (AR) based android app that can project the dimensions of an automobile in the real world and recognize voice commands to operate functions like opening car doors and changing colors. The app uses a combination of augmented reality, machine learning technology, Unity game engine, C# script, Google speech recognition API and Vuforia SDK to superimpose images of the car in the real world and allow control through voice commands. The initial focus is on cars, but the solution can also be used to create AR-enabled brochures for marketing companies to enhance sales and provide customers with a better understanding of the product before purchase. © 2023 IEEE.

关键词： Speech recognition

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Efficient Skin Lesion based Classification System for Monkeypox Detection using VGG16 and Ensemble Learning 10

Efficient Skin Lesion based Classification System for Monkey...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Pabbi, Varinder Khullar, Vikas Angurala, Mohit Ramgarhia Institute of Engineering and Technology Department of Computer Applications Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789380544472

Monkeypox is a recently emerged disease outbreak that has impacted the health of many individuals. This study focuses on developing a skin lesion-based classification system for monkeypox disease. Many recent works suggest that the direct implementation of machine learning (ML) and deep learning (DL) approaches for this task has not resulted in high accuracy. Therefore, this paper proposes an efficient technique that employs a hybrid of VGG16-based feature engineering, class balancing, and ensemble learning for the detection of monkeypox skin lesions. Specifically, DL-based feature extraction is applied, but the classification is carried out through ensemble learning techniques. The results demonstrate that the random forest, extra tree, and histogram gradient descent methods achieved better performance in terms of different performance metrics. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Feature extraction

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Facial recognition using grey wolf optimization

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Materials Today: Proceedings 2022年 58卷 273-285页

作者： Barman, Bhaswati Dewang, Rupesh Kumar Mewada, Arvind Computer Science and Engineering Department Motilal Nehru Institute of Technology Allahabad Prayagraj India

Nowadays, multimedia data on social media platforms of the Internet, especially images, are proliferating, due to which image retrieval systems based on content are one of the major research areas. Despite the introduction of various content-based images retrieval approaches, face recognition remains a problematic task. Consequently, this paper developed a face recognition framework using modifying the bag-of-features (BOF) from a grey wolf optimisation (GWO) algorithm in which a variable weight is used. Variable weight proposes a sine cos function to find the best score. The convergence behaviour of the variable weight GWO algorithm is performed over some benchmark functions. Furthermore, it has been used to get the histograms fed into training and testing function to get the model accuracy using the Yale face database. © 2022

关键词： Face recognition

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Optimizing Agricultural Productivity: Developing a Smart Crop Recommendation System Using Machine Learning 2

Optimizing Agricultural Productivity: Developing a Smart Cro...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Nokshangthemba Kumar, Saurabh Soloman, Shyam Sunder Jannu Baydeti, Nagaraju National Institute of Technology Nagaland Department of Computer Science and Engineering Nagaland Chumukedima797 103 India

ISBN: (纸本)9798350366570

Crop recommendation is the crucial aspect of modern agriculture, aiming to assist farmers in selecting the most suitable crops for their land and maximizing yield. In this study, the effectiveness of various preprocessing techniques, grid search optimization and the pipeline class are applied in conjunction with the different machine learning algorithm for the crop recommendation. The accuracy and efficiency of the recommendation process enhancement is aimed by applying techniques including data standardization, relative density normalization, and grid search optimization. Leveraging Machine Learning algorithms within a unified pipeline framework allows for systematic evaluation and comparison of models, ultimately providing farmers with reliable recommendations for crop selection. Machine Learning algorithms Decision Tree, K-Nearest Neighbor, Random Forest, Support Vector Machine, Logistic Regression and Naïve Bayes were applied to predict and optimize agricultural productivity based on the dataset collected from Nagaland state. Among these algorithms, Random Forest outperformed the others, achieving an impressive accuracy of 99.54% in predicting agricultural productivity. © 2024 IEEE.

关键词： Smart agriculture

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Study on the Compression Property of Formation Space for Theoretical Support

Study on the Compression Property of Formation Space for The...

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Rajesh Kanna, R. Sharmila, L. Senthil, K. Umadevi, G. Raja, R. Vinston Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram India Agni College of Technology Department of Computer Science and Engineering Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering India Panimalar Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350394580

At present, the open stopping with subsequent filling mining of the 'two step' mode has received significant attention. The recovery method entails dividing the ore block into the first step ore room and the second step ore room, mining the first step ore room initially and filling the formation space using cemented materials and then mining the second step ore room and filling the formation space using tailings. There are two questions to resolve in the tailings-filling work of a goaf. The first is making an accurate filling plan to assure the accomplishment of the work on time, which will not affect the recovery progress. The other is accurately calculating the lateral pressure that tailings exert on the cemented fill body to provide theoretical support for designing the required strength of cement filling body to assure its stability and the safety of the recovery process of second step ore room. These questions are associated with the density of the tailings. However, the open stoping with subsequent filling mining method is characterized by a high stage and long span. When the recovery of ore room is finished, the volume of the resulting goaf is large. The density of the tailings is affected by its weight and this exhibits non-linear growth as the height of the tailings increases. Therefore, to solve these questions, research is needed on the compressibility of the tailings under its weight. © 2023 IEEE.

关键词： Compacting rule, Model Self-compacting behavior Tailings

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Identifying The Established Semantic Kinds In Text Using Named Entity Recognition

Identifying The Established Semantic Kinds In Text Using Nam...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Seershika, Mitnasala Aishwarya, Vasuri Vani, V. Karthik, N. National Institute of Technology Puducherry Department of Computer Science of Engineering Puducherry Karaikal609 609 India

ISBN: (纸本)9798331540685

Named entity recognition (NER) is a natural language processing (NLP) method that extracts information from text. NER includes identifying and ordering significant data in text known as named elements. By approaching span-based method (where tokens are represented as spans) we can detect named entity for each span. However some current algorithms check every span separately without knowing previous span, which leads to ambiguity. To overcome this, a bi-directional two-dimensional operation is used, which uses knowledge of previous spans and compares the two spans and generates meaningful named entities. For implementation of the NER we have used the three public datasets. The experimental results in this paper shows that the method we used is very effective to generate meaningful nested named entities. © 2024 IEEE.

关键词： Named Entity Recognition Planarized sentence Self-Encoding

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CNN-LSTM based classification of polo like kinase family of Proteins: An emerging cancer drug target

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Materials Today: Proceedings 2022年 58卷 445-450页

作者： John, Chinju Mathew, Oommen K. Sahoo, Jayakrushna Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam686635 India

Artificial intelligence and deep learning are becoming an inevitable part of our life. Deep learning models are giving contributions to the identification of genetic causes behind various diseases affecting the human community. The prognosis and diagnosis of such diseases can be recommended with the help of artificial intelligence. Here we are proposing a novel deep learning model that employs the legendary deep learning architectures such as convolutional neural network and the variant of recurrent neural network known as long short-term memory network to classify the protein sequences belonging to the five-member polo like kinase family, a subclass of Serine-Threonine kinases, which is considered as an active anti-cancer drug target these days. The proposed deep learning model was trained and tested on sequences collected from biological sequence databases and classified the new sequences to their corresponding classes with an accuracy of 97.6%. Furthermore, this model could untangle the efforts associated with sequence annotation and classification, which is always a tedious and exigent task. © 2022

关键词： Diseases

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Exploiting machine learning and deep learning models for misbehavior detection in VANET

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International Journal of computers and Applications 2022年第11期44卷 1024-1038页

作者： Sultana, Rukhsar Grover, Jyoti Meghwal, Jitesh Tripathi, Meenakshi Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur Jaipur India

There is an unprecedented proliferation of vehicular traffic data as the number of interconnected vehicles has increased in Vehicular Ad Hoc Networks (VANET) scenario to realize the goals of Intelligent Transportation Systems (ITS). As a result, the quantity and variety of attacks using transmitted data have risen. However, these broadcasted messages can be used to launch many security attacks as these messages are not encrypted. With the increase in the number of vehicles, the risk of misbehavior also increases as more malicious vehicles broadcast inaccurate and faulty messages. Various trust-based and deterministic misbehavior detection techniques have been proposed to identify such misbehavior. Existing solutions only address specific types of network intrusions. However, because VANET situations may involve a wide spectrum of attacks, a more robust misbehavior detection mechanism is required. This article compares the performance of several machine learning and deep learning architectures using key evaluation metrics. Furthermore, we have demonstrated a comparison of performance metrics with existing learning model-based techniques in different network scenarios. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Vehicular ad hoc networks

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A Comprehensive Survey on Grey Wolf Optimization

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Recent Advances in computer Science and Communications 2022年第3期15卷 323-333页

作者： Sharma, Isha Kumar, Vijay Sharma, Sanjeewani Department of Computer Science and Engineering National Institute of Technology Himachal Pradesh Hamirpur India

Grey wolf optimizer is a recently developed metaheuristic algorithm that mimics hunting and social behaviour. It has been applied in most of the engineering design problems. Grey wolf optimizer and its variants have been effectively used to solve real-life applications. For some com-plex problems, a grey wolf optimizer has been hybridized with other metaheuristics. This paper summarizes the overview of the grey wolf optimizer and its variants. The pros and cons of these variants have been discussed. The application of a grey wolf optimizer has also been discussed with future research directions. This paper will encourage the researchers to use this algorithm for their real-life problems. © 2022 Bentham Science Publishers.

关键词： Optimization

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A Study on Design, Development and Deployment of Web Crawler Algorithms and Their Metrics

A Study on Design, Development and Deployment of Web Crawler...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Arthy, J. Raja, K. Srm Institute Of Science And Technology Department Of Computer Science And Engineering Chennai Tamilnadu Ramapuram600089 India

ISBN: (纸本)9798350364828

Globally, the search engine is extremely important in reducing the difficulty of information exploration. An internet spider, bot, or program known as a web crawler is used by search engines to maintain an index and retrieve online sites in response to user queries. Web pages are really crawled in order to be explored, starting from the seed URL. In this study, the standard protocols of the literature survey were followed to describe the crawler mechanism also analyzed and compared several research publications to explain the crawler types, individual architectural types, policies, methodologies, metrics, techniques, and equipments for every category was covered. Additionally, the maximum crawler algorithms and the open-source crawler tools were listed and described. This paper gives insight into choosing the optimal crawler algorithm for search engine optimization and for various applications. © 2024 IEEE.

关键词： Web crawler

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