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Unmasking Fakes: A machine Learning and Blockchain Solution 3

Unmasking Fakes: A Machine Learning and Blockchain Solution

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Srinivasan, Deepak Krishna Dibakaran, K. Praveen, R.K. Sripriya, S. K.S. Rangasamy College of Technology Department of Artificial Intelligence and Data Science Tiruchengode India

ISBN: (纸本)9798331509675

In today's digital landscape, a vast number of users engage with product network websites, leading to the registration of thousands of new accounts daily. These platforms facilitate constant interaction among users, but they also pose risks to consumer data security. Despite these problems, product networks have a considerable number of advantages. To neutralize the security risks, there is a necessity of profiling on these networks, so that whether consumers are vulnerable to these risks or not would be analyzed. Accurately distinguishing between legitimate and malicious profiles can greatly enhance the security of these networks. During this research, a number of classification techniques have been used to detect malicious profiles up to an accuracy of 85%. However, The technique overperforms these methods because it uses more sophisticated machine learning and natural language processing techniques, thus contributing to improved detection rates. Algorithms like Naive Bayes, Support Vector machine (SVM), Gradient Boosting, Logistic Regression, and Random Forest are stated to be 90% accurate. So, this will surely be ahead of any system that exists in the market. © 2025 IEEE.

关键词： Adversarial machine learning

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Wearable Location Tracker During Disaster using AI and IoT 3

Wearable Location Tracker During Disaster using AI and IoT

引用

3rd international conference on Applied Artificial intelligence and Computing, ICAAIC 2024

作者： Praveen, P. Kumar, K. Pranay Sr University School of Computer Science & Artificial Intelligence Telangana State Warangal India

ISBN: (纸本)9798350375190

The combination of Artificial intelligence (AI) and the Internet of Things (IoT) has improved the capabilities of location trackers, especially in disaster management. This brief explores the collaboration between AI and the Internet of Things to improve the efficiency and effectiveness of tracking devices in the field in the event of an incident. Wearable location trackers equipped with IoT sensors collect real-time data on the wearer's movements, environmental conditions, and vital signs. AI algorithms process this data, enabling the device to distinguish between normal activities and emergency situations. machine learning algorithms enable the device to learn from historical data, improving its predictive capabilities and reducing false alarms. This design of GPS location tracking is through a satellite. It includes AI and IoT for receiving the data in case of disaster. To view the location, the person can use any device connected to the Internet. The parameters we can find through this are the scalable map of the surrounding area, latitude, longitude, speed, and altitude. © 2024 IEEE.

关键词： Internet of things

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Hotel Energy Management Optimization System Based on Artificial intelligence 3

Hotel Energy Management Optimization System Based on Artific...

引用

3rd international conference on Artificial intelligence and Autonomous Robot Systems, AIARS 2024

作者： Wang, Yong Chen, Jia Wuhan Vocational College of Software and Engineering Wuhan Open University Hubei Wuhan430205 China

ISBN: (纸本)9798350376173

This study is devoted to the design, implementation and evaluation of an Artificial intelligence (AI)-based hotel energy management optimization system (EMOS), aiming at improving energy efficiency, reducing operating costs and providing scientific and intelligent solutions for the sustainable development of the hotel industry. The system integrates machine learning, data analysis and intelligent control technology. Through real-time monitoring, forecasting and adjustment, energy consumption can be more intelligent and flexible to adapt to different operating conditions. In the aspect of experimental deployment, this study selected an actual hotel as a pilot, deployed the hotel EMOS based on AI, and conducted comparative experiments in different time periods. The results show that the system significantly improves the energy efficiency of the hotel. Through the machine learning algorithm, the system established an accurate energy forecasting model, and realized the accurate prediction of future energy demand. Compared with the traditional baseline system, the intelligent system shows higher energy efficiency in each time period, which provides substantial economic benefits for the hotel. EMOS, an AI-based hotel, shows excellent performance in improving energy efficiency and reducing operating costs. © 2024 IEEE.

关键词： Artificial intelligence Energy Management Optimization System Hotel

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Data Mining and Artificial intelligence Applications in Engineering Geology and Geological Experimental Testing 3

Data Mining and Artificial Intelligence Applications in Engi...

引用

3rd international conference on Artificial intelligence and Autonomous Robot Systems, AIARS 2024

作者： Qi, Daqian Ci, Zenghui Dong, Fuyong The Fifth Prospecting Team of Shandong Coal Geology Bureau Jinan China

ISBN: (纸本)9798350376173

How to effectively mine information on geological structures, soil mechanics, groundwater, etc. becomes a topic worth studying. This article combines data mining and artificial intelligence technology to solve the problems of data processing and decision support in engineering geology and geological experimental testing. The article collects a large amount of geological structure data, soil mechanics data and groundwater data, uses feature selection and extraction technology to extract the most representative and relevant features from the original data, and provides engineering geological decision support based on model prediction results, such as geological disaster early warning, land development assessment, etc. Experimental tests show that the recall rate of this method is as high as 98%. The use of machine learning algorithms and statistical models can more accurately predict soil mechanical parameters and groundwater level changes, providing strong support for engineering design and decision-making. © 2024 IEEE.

关键词： Soil testing

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Melody Extraction from MIDI Files Using AI-ML Techniques 3

Melody Extraction from MIDI Files Using AI-ML Techniques

引用

3rd international conference for Innovation in Technology, INOCON 2024

作者： Navita Kumar, Ashok Meitei, Chanambam Mokaju Sharda University Center for Artificial Intelligence in Medicine Imaging & Forensics Dept of Physics Greater Noida India

ISBN: (纸本)9798350381931

Development of Artificial intelligence and machine Learning (AI-ML) based automated tools for melody extraction from audio music files are severely handicapped due to the scarcity of proper musical annotation to act as labels for supervised learning. For many kinds of traditional music the melodies can be quite intricate, and detailed annotation is not only scarce but also quite difficult to create. MIDI music files on the other hand carry within them the notes of melody which if extracted can act as musical annotations for training supervised melody extraction tools. In this study, several machine Learning based classifiers were trained to determine whether a particular channel of MIDI file carries a melody or not. The six features used for training were Average TFIDF, Overlap, Number of Unique Notes, Note Duration, Note Velocity and Entropy. The testing accuracies obtained by Support Vector machine (SVM), Random Forest, Logistic Regression, and Artificial Neural Network (ANN) classifiers were 86.67%, 88.64%, 86.91% and 93.80% respectively, with ANN based method giving the best testing result. © 2024 IEEE.

关键词： Extraction

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SaaS-based Intelligent conference Management System using Facial recognition 3

SaaS-based Intelligent Conference Management System using Fa...

引用

3rd IEEE international conference on Information Technology, Big Data and Artificial intelligence, ICIBA 2023

作者： Ling, Yue Wuxi City College of Vocational Technology Huishan District Wuxi214153 China

ISBN: (纸本)9781665490788

An SaaS-based conference management system that implements facial recognition technology has been designed to address the requirement for intelligent conference management. The system leverages the SaaS model to achieve rapid deployment, enabling centralized management of multiple conference venues via a conference management service center. This center enables unified scheduling of attendees, equipment, and conference facilities, and provides customization to meet the requirements of various conference scenarios. The system uses facial recognition technology for conference check-in and data collection during the event, thereby optimizing conference automation management based on attendee information. User feedback has indicated a significant improvement in conference management efficiency. © 2023 IEEE.

关键词： Face recognition

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Proceedings of the 3rd international conference on Automated machine Learning, AutoML 2024

Proceedings of the 3rd International Conference on Automated...

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3rd international conference on Automated machine Learning, AutoML 2024

The proceedings contain 19 papers. The topics discussed include: is Mamba capable of in-context learning?;HPOD: hyperparameter optimization for unsupervised outlier detection;speeding up NAS with adaptive subset selection;confidence interval estimation of predictive performance in the context of AutoML;analyzing few-shot neural architecture search in a metric-driven framework;FLIQS: one-shot mixed-precision floating-point and integer quantization search;improving transfer learning by means of ensemble learning and swarm intelligence-based neuroevolution;sequence alignment-based similarity metric in evolutionary neural architecture search;and don’t waste your time: early stopping cross-validation.

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Deep Learning-based Plant Species Identification using VGG16 and Transfer Learning: Enhancing Accuracy in Biodiversity and Agricultural Applications 3

Deep Learning-based Plant Species Identification using VGG16...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kiran, K. Kaur, Arpanpreet School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798331527549

In this paper, the method of deep learning is applied for plant species identification, with a special emphasis on Convolutional Neural Network VGG16 for feature extraction and classification. Identification of plants is important in matters concerning biological diversity, agriculture as well as management and utilization of resources but the conventional methods used in identification are time consuming and sometimes inaccurate. machine learning specifically deep learning is a more suitable replacement by offering a fast and accurate identity recognition. The research work works with a Kaggle dataset comprising of 24881 images of four plant species categorized into twenty-two with the objectives of increasing efficiency of the classification. We used transfer learning with weights from the ImageNet data set and obtained accuracy greater than 90% on the test data set, which shows the model’s ability to identify plant species based on leaf structure. To counter overfitting and enhance model generality, data augmentation methods were employed where the input images were rotated by 30, 45 and 60 degrees, and flipped vertically. The performances suggest that deep learning models, such as VGG 16, yield improved recognition for plant classification than conventional methods provide higher reliability. This research makes a valuable input toward a number of applications related to automated systems for agriculture and conservation, discussing possible uses of AI technique for better penetration of plant species, enhancing conservation goals, and future advancements in the agrarian field. Future research will leverage on these discoveries to make improvements on classification frameworks. © 2025 IEEE.

关键词： Plant diseases

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Artificial intelligence Powered Early Detection of Heart Disease 3

Artificial Intelligence Powered Early Detection of Heart Dis...

引用

3rd international conference on Smart Data intelligence, ICSMDI 2023

作者： Mohanaprakash, T.A. Abirami, P. Navaneethakrishan, M. Savija, J. Ramya, M. Anbarasa Pandian, A. Panimalar Engineeering College Department of Cse Tamilnadu Chennai India Department of Cse Velammal Institute of Technology Tamilnadu Chennai India St.Joseph College of Engineering Department of Cse Tamilnadu Sriperumbudur Chennai India Loyola Institute of Technology Tamilnadu Chennai India

ISBN: (纸本)9781665464871

In the healthcare industry, machine Learning (ML) plays a crucial role in disease prediction. A patient must go through a series of tests before a condition can be diagnosed. However, using machine learning techniques, the number of tests can be reduced. This simplified test has a significant impact on both time and performance. Early patient care has benefited from sound medical data analysis due to the growing amount of data generated by the medical and healthcare sectors. With the help of disease data, massive amounts of medical data can be mined for hidden pattern information. With a focus on heart diseases, this study evaluates and suggests a heart disease prediction based on the patient's symptoms using machine learning techniques such as SVM, MLR, and RF algorithms. The proposed method outperforms those currently in use in terms of accuracy, forecast speed, and consistency of outcomes. It is also appropriate to classify lung cancers using trained datasets for accurate identification. © 2023 IEEE.

关键词： Diseases

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TSR-YOLO: Multi-Stage Self-Processing Method for Small Traffic-Signs recognition 24

TSR-YOLO: Multi-Stage Self-Processing Method for Small Traff...

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3rd international conference on Cyber Security, Artificial intelligence and Digital Economy (CSAIDE)

作者： Wang, Futian Lv, Weijie Tang, Jin Lu, Andong Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Multimodal Cognit Computat Hefei 230039 Anhui Peoples R China Hefei Comprehens Natl Sci Ctr Inst Artificial Intelligence Hefei 230000 Anhui Peoples R China

ISBN: (纸本)9798400718212

Traffic signs play a critical role in the transportation infrastructure, reducing accident risks by informing drivers, pedestrians, and other road users about roadway conditions. With rapid advancements in computer vision and artificial intelligence, traffic sign recognition systems have become increasingly integrated into driver assistance and autonomous driving systems. However, recognizing small traffic signs in real-world applications poses a considerable challenge due to information loss in feature extraction, limited available information, and large scale variations This paper presents a network structure, TSR-YOLO, that efficiently recognizes small-sized traffic signs. Initially, our research found that FPN and its variants place too much emphasis on the interaction between different feature maps and overlook their individual processing capabilities. We design a multi-stage perceptual self-processing module and a new FPN structure to boost the spatial and contextual information of features. To improve fusion of semantically and scale-inconsistent features, we suggest a multi-scale attention module that effectively resolves the issue of merging different features. Experiments on the challenging TT100K dataset show that our model outperforms popular object detection models by 4.2% when compared to the original YOLOV5, while preserving real-time speed.

关键词： Traffic signs Small object detection Multi-stage self-processing Feature fusion

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