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Smart Fashion Recommendation System using Random Forest Algorithm

Smart Fashion Recommendation System using Random Forest Algo...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Shivangi Patel Arneet Kaur Theertha Sudheer Babu Nair Shamneesh Sharma Vikas Verma Nishant Agnihotri School of Computer Science and Engineering Lovely Professional University Punjab India Delivery and Student Success UpGrad Education Private Limited Bangalore Karnataka India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

The fashion sector is expanding quickly in the digital age, and this means that more sophisticated recommendation systems that improve user experiences and expedite the online purchasing process are required. This work proposes a sophisticated and all-inclusive fashion recommendation system that uses machine learning techniques, specifically Random Forest, to enhance suggestion accuracy and efficiency. Users can choose their preferred fit and style using an interactive interface, which enables the system to offer clothes based on their individual profiles. Moreover, Context-based filtering involves personalizing recommendations to users based on a variety of contextual factors which may include user preferences, current fashion trends, occasion, style, body type and others. As a result of the dynamic recommendation process, the recommendations are constantly improved and grow more specific and well-rounded over time. The outcomes showcase the system's capacity to effectively combine various machine learning algorithms to evaluate a wide range of data sources, including user preferences, item features, and contextual information, to provide individualized recommendations.

关键词： Training Measurement Accuracy Machine learning algorithms Soft sensors Clothing Market research Information age Recommender systems Random forests

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The Implementation of Big Data With Cloud and Edge Computing in Enhancing the Smart Grid Information Processes Through Sem Model 2

The Implementation of Big Data With Cloud and Edge Computing...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Chahar, Ankit Christobel, Y. Angeline Ritika Adakane, Prashant K. Sonal, Deepa Tripathi, Vikas R.V. College of Engineering Student Department of Computer Science and Engineering Karnataka Bengaluru India School of Computational Studies Hindustan College of Arts & Science Chennai India G.L Bajaj Institute of Technology & Management It Department Greater Noida India G H Raisoni University Department of Computer Science and Engineering Amravati India Patna Women's College Department of Computer Science Bihar Patna800014 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

It is not possible to handle and huge amounts of data using present software programmes and personal computers owing to a lack of appropriate computing power. Manufacturing organisations must either employ Cloud computing technology or engage in more current techniques including Edge Computing technology if they are to compete in the global market. The SEM model is used to examine the Smart Grid Information Processes in this article. Furthermore, it is proposed that the research contributes to the expansion of experience in the discipline of smart meters and Internet of Things acceptability, despite the fact that this is a very rare topic. © 2022 IEEE.

关键词： Smart power grids

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Bi-Directional Conversion Algorithm of Database Based on Artificial Intelligence

Bi-Directional Conversion Algorithm of Database Based on Art...

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Data science and Information System (ICDSIS), IEEE International Conference on

作者： Ning Tao Lin Geng Chong Xu School of Computer Science and Engineering Shenyang Jianzhu University Shenyang Liaoning China Lin Geng Department of Student Affairs Shenyang Jianzhu University Shenyang Liaoning China

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

The traditional database conversion algorithm is difficult to implement, expensive and difficult to popularize. The lack of conversion algorithm directly affects the result of data integration. The core problem of the mutual transformation between XML(eXtensible Markup Language) and relational data is how to construct the mapping between relational database and XML document, that is, the mapping between objects and relationships. In this paper, the element tree is used to transform the probabilistic relational data model and the probabilistic XML data model in heterogeneous databases. First, the corresponding relationship between the element tree and the unit nodes and various data models is established. Then, on this basis, the bidirectional transformation of the data model is realized through the mapping relationship between the element tree and the data. When the transaction sending frequency is 400 transactions $/ \mathrm{s}$, the write throughput is 89TPS and the query throughput is 185TPS. The query and write performance of the database bidirectional conversion algorithm based on artificial intelligence is better.

关键词： Databases XML Information sharing Transforms Relational databases Probabilistic logic Throughput

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Monitoring patients suffering from restless leg syndrome (RLS) using IoT

Monitoring patients suffering from restless leg syndrome (RL...

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International Conference on Control, Instrumentation, and Automation

作者： Ali Sioofi Maral Samimi Mehrshad Khosravi Armin Niakousari Ali Akbar Safavi Student research committee Shiraz University of Medical Science Shiraz Iran School of Electrical and Computer Engineering Shiraz University Shiraz Iran

Restless legs syndrome is a common neurological disorder that is often not diagnosed. It causes unusual symptoms that usually happen in the evening and at night. This can lead to problems with sleep and daily activities. It presents in different ways and can be more serious for certain groups of patients. It is important to accurately diagnose and treat this disorder. It can be difficult to figure out if someone has restless legs syndrome, especially kids and old people. Therefore, this study aims to present a tool for monitoring abnormal movements related to restless leg syndrome. An IOT-based platform with “ESP32” board and “MPU6050” module with the “Online Web server” method are used to monitor the movements. The designed platform accurately detects any vibrations and movements associated with this syndrome without any wired or complicated hardware connected to the person. This IOT based online monitoring system proved effective and can be used for long-term monitoring.

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Human Activity Recognition: Fight Detection using Long term Recurrent Convolutional Network (LRCN) 5

Human Activity Recognition: Fight Detection using Long term ...

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5th International Conference on Data Analytics for Business and Industry, ICDABI 2024

作者： Jhnvi, Ngrddy Bhrdwj, S.K. Sharma, Ajay Sharma, Shamneesh Sodhi, Ankur Gochhait, Saikat Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India Delivery and Student Success upGrad Education Private Limited Karnataka Bangalore India Enterprise Businees and Career Services upGrad Education Private Limited Karnataka Bangalore India Constituent of Symbiosis International Deemed University Symbiosis Institute of Digital and Telecom Management Pune India

ISBN: (纸本)9798350368710

Human Activity Recognition for Fight Detection is an important research domain aimed at automatically identifying patterns indicative of physical altercations. Leveraging deep learning models like CNNs and RNNs, this approach extracts features from video frames to recognize fight-related behavioural patterns. The primary objective is to develop a machine learning model capable of autonomously detecting instances of fights in video footage, using techniques such as the Long-term Recurrent Convolutional Network (LRCN). Training involves a comprehensive dataset encompassing examples of both fights and non-fight activities, with model performance evaluated using standard metrics. Each frame undergoes individual analysis by the model to predict the presence of any indications of a fight. The model predicts the action with an accuracy of 98.03% and the movements given as input can be categorized as fights or no fights. © 2024 IEEE.

关键词： Deep learning

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IPL Data Analysis and Visualization for Team Selection and Profit Strategy

IPL Data Analysis and Visualization for Team Selection and P...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Saranya G Aravind Swaminathan Joel Benjamin J Surendran R Leema Nelson Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai campus Chennai India MSc student Data Science Middlesex University Dubai Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Chitkara University Institute of Engineering & technology Chitkara University Punjab India

Day by day, the role of data science and machine learning in cricket is increasing due to the large amount of data generated from a single player on a whole line. The field of data science is the intensive study of data to extract insights and knowledge from the data and apply the acquired knowledge and actionable insights. These available data and statistics are used to predict things like the team's first-innings score and the probability of winning the second team, etc. This study works on Indian Premier League (IPL) Data Analysis 2022 and Data Visualization for the duration (2008–2020) using Python. This application modules are followed by preprocessing, data analysis, and visualization, and finally create a model that predicts the team's overall score and the probability of winning. When building models, python algorithms such as Numpy is used for Scientific Computing, Pandas used to perform Data Analysis, and finally Matplotlib and Seaborn to enable Data Visualization. Finally, this paper helps the trainers and owners of the team during the auction to select an emerging player to win the matches in the entire season and to win the trophy and to get profit to the owners by the profit strategy done in this analysis report.

关键词： Analytical models Scientific computing Computational modeling Data visualization Machine learning Data science Probability

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Beyond Traditional Methods: Exploring Deep Learning Techniques for Accurate Traffic Projection

Beyond Traditional Methods: Exploring Deep Learning Techniqu...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Avinash Jha Shresthi Singh Shamneesh Sharma Isha Batra Arun Malik Saira Muzzaffar Yadaiah Nirsanametla School of Computer Science and Engineering Lovely Professional University Punjab India Delivery and Student Success upGrad Campus upGrad Education Private Limited Bangalore India School of Computer Science Taylor's University Subang Jaya Malaysia Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Predicting traffic flow is a crucial component of the intelligent transportation system. This is a precise projection of the volume of traffic in a specific area at a specific future period. Researching traffic forecasts is beneficial in reducing traffic and facilitating safer, more affordable travel. Although classic models make use of shallow networks, the number of cars has grown exponentially in recent years, making these traditional machine learning techniques inapplicable to contemporary situations. We discuss some of the most recent developments in deep learning for traffic flow prediction in our article. The Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Restricted Boltzmann Machines (RBM), and Stacked Auto are among the many deep learning designs. Encoder (SAE). These deep learning models gradually extract higher level information from raw input by using numerous layers. The most recent deep learning models created to address this specific issue are examined, and because transport networks are complicated, this review also informs the reader about the aspects that affect these models and which models perform best in particular situations.

关键词： Deep learning Recurrent neural networks Reviews Computational modeling Market research Convolutional neural networks Data mining Automobiles Long short term memory Intelligent transportation systems

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HTML Smuggling: Attack and Mitigation

HTML Smuggling: Attack and Mitigation

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Security of Information and Networks (SIN), International Conference on

作者： Keshav Kaushik Govind Sharma Preeti Narooka Gunjan Chhabra Ankit Vishnoi School of Computer Science University of Petrolem and Energy Studies Dehradun India Student School of Computer Science University of Petrolem and Energy Studies Dehradun India Department of AIML School of Computer Science and Engineering Manipal University Jaipur Jaipur India Department of CSE Graphic Era Hill University Dehradun India Computer Science and Engineering Symbiosis Institute of Technology (SIT) Pune Symbiosis International (Deemed) University Pune India

HTML smuggling is a type of web-based attack that exploits vulnerabilities in web applications to deliver malicious content to unsuspecting users. This attack has become increasingly popular in recent years, as cybercriminals continue to look for new ways to circumvent security measures and deliver malware to users. Mitigating HTML smuggling attacks is a complex task, as it requires a deep understanding of web application security and the ability to identify and mitigate vulnerabilities in web applications. In this research paper, the authors provide a comprehensive overview of HTML smuggling attacks and their mitigation. They explore the techniques used by attackers to carry out the attack, including the use of steganography and obfuscation to hide malicious code within legitimate HTML code. They also discuss the impact of the attack on web applications and users, including the delivery of various types of malwares.

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An AI-Centred Adaptive Cognitive Network Traffic Modeling & Measurement Technique for EIS 3

An AI-Centred Adaptive Cognitive Network Traffic Modeling & ...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Bharathi, L. Vasoya, Nimish H. Kansal, Lavish Gehlot, Anita Swathi, Bajila Ahmed Khan, Suyeb Ramachandra College of Engineering Department of Electronics and Communication Engineering Andhra Pradesh Eluru India Children's University Department of Student Startup Gujarat Gandhinagar382021 India Lovely Professional University Phagwara India Uttaranchal University Uttaranchal Institute of Technology India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Baba Kuma Singh Ji Engineering College Amritsar India

ISBN: (纸本)9798350399264

The IoT is the foundation of the Enterprise Information System (EIS), which collects a lot of business information. For EIS, timely, accurate data analysis & transfer are crucial. For net management & traffic optimization in EIS, Network infrastructure traffic is crucial. Nevertheless, the needs for fine-grain traffic monitoring are in conflict with the monitoring overheads & precision. Long-standing promises of artificial intelligence (AI) include the ability to recognise the physical attributes of network activity & take appropriate steps in response. In order to decrease network monitoring overheads & increase measurement precision, we present an AI centred between light Adapt Measuring System (ALAMM) for SDN in this study. Initially, we simulate & forecast the network congestion information using AI. We suggest an adaptive strategy to choose the rate of traffic sample centred on the outcomes of traffic forecasting. Next, we instruct switching devices to evaluate the coarse-grain traffic of flow pattern & networks using the measuring primitives. In order to retrieve the Fine grained (FG) assessment & optimise the measured values, the matrices refilling & optimizing approach is finally suggested. Simulated outcomes demonstrate that our method can accurately and efficiently determine network activity. © 2023 IEEE.

关键词： Software defined networking

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Advancements in Plant Disease Detection: Leveraging Convolutional Neural Networks for Bell Pepper Health Assessment

Advancements in Plant Disease Detection: Leveraging Convolut...

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Agrosystem engineering, Technology & Applications (AGRETA), IEEE International Conference on

作者： Harsh Vazirani Xiaofeng Wu Anurag Srivastava Debajyoti Dhar Divyansh Pathak Sanskaar Srivastava School of Aerospace Mechanical and Mechatronic Engineering University of Sydney Sydney New South Wales Australia Department of Engineering Sciences ABV-Indian Institute of Information Technology and Management Gwalior India Student in Computer Science ABV-Indian Institute of Information Technology and Management Gwalior India Student in Chemical Engineering Indian Institute of Technology Kanpur India

ISBN: (数字)9798350373660

ISBN: (纸本)9798350373677

In the field of plant-based disease diagnosis, convolutional neural networks (CNNs) have become an essential tool due to their exceptional ability to recognize sick foliage. With an emphasis on bell pepper plants specifically, this study provides a thorough examination of modern plant disease detection technologies. Several modeling techniques were investigated, with a particular emphasis on neurons and layers, by utilizing CNNs that were trained on a carefully selected dataset of photos of bell pepper plants. Since that early disease identification has a direct impact on crop quality, yield, and economic viability, its importance in agriculture cannot be emphasized. Our methodology, which makes it easier to identify sick bell pepper plants quickly, is a key component of proactive management plans that reduce agricultural losses and stop the spread of disease. This study emphasizes how critical it is to eliminate diseased plants as soon as possible in order to prevent illness from spreading across the garden and preserve crop health in general. As a result of these efforts, our CNN model was able to attain an incredible 96.5% accuracy, highlighting its potential to be a game-changing instrument in contemporary agriculture.

关键词： Training Plant diseases Accuracy Plants (biology) Neurons Crops Agriculture Convolutional neural networks Standards Testing

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