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14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

作者： Larson, Jacoby Trivedi, Mohan Univ Calif San Diego Comp Vis & Robot Res Lab La Jolla CA 92093 USA

ISBN: (纸本)9781457721977

In order for an autonomous unmanned ground vehicle (UGV) to drive in off-road terrain at high speeds, it must analyze and understand its surrounding terrain in real-time: it must know where it intends to go, where are the hazards, and many details of the topography of the terrain. Much research has been done in the way of obstacle avoidance, terrain classification, and path planning, but still so few UGV systems can accurately traverse off-road environments at high speeds autonomously. One of the most dangerous hazards found off-road are negative obstacles, mainly because they are so difficult to detect. We present algorithms that analyze the terrain using a point cloud produced by a 3D laser range finder, then attempt to classify the negative obstacles using both a geometry-based method we call the Negative Obstacle DetectoR (NODR) as well as a support vector machine (svm) algorithm. The terrain is analyzed with respect to a large UGV with the sensor mounted up high as well as a small UGV with the sensor mounted low to the ground.

关键词： 3D laser range finder Equations LIDAR based off-road negative obstacle detection Laser radar Lasers NODR Robot sensing systems svm algorithm Support vector machines Three dimensional displays UGV Vehicles autonomous unmanned ground vehicle geometry geomet

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"Mahoshadha", the Sinhala Tagged Corpus Based Question Answering System

"Mahoshadha", the Sinhala Tagged Corpus Based Question Answe...

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1st International Conference on Information and Communication Technology for Intelligent Systems (ICTIS)

作者： Jayakody, J. A. T. K. Gamlath, T. S. K. Lasantha, W. A. N. Premachandra, K. M. K. P. Nugaliyadde, A. Mallawarachchi, Y. Sri Lanka Inst Informat Technol Malabe Sri Lanka

ISBN: (纸本)9783319309330;9783319309323

"Mahoshadha" the Sinhala Question Answering Systems aims at retrieving precise information from a large Sinhala tagged corpus. This paper describes a novel architecture for a Question Answering System which summarizes a tagged corpus and uses the summarization to generate the answers for a query. The summarized corpuses are categorized according to a set of topics enabling fast search for information. K-Nearest Neighbor algorithms is used in order to cluster the summarized corpuses. The query will be tagged, the tagged query will be used to get more accurate results. Through the tagged query the question will be identified clearly with the category of the query. Support Vector Machine is used in order to both automate the summarization and question understanding. This will enable "Mahoshadha" to answer any type of query as well as summarize any type of Sinhala corpus. This enables the Question Answering System to be more useable through many applications.

关键词： Question answering Document summarization Document categorization svm algorithm k-NN classification

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Air Quality Monitoring System Based on Artificial Intelligence 1st

Air Quality Monitoring System Based on Artificial Intelligen...

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1st International Conference on Advances in Signal Processing and Communication Engineering (ICASPACE)

作者： Sowmya, Vattam Ragiphani, Shravya BV Raju Inst Technol Dept Elect & Commun Engn Hyderabad Telangana India Sri Devi Womens Engn Coll Hyderabad Telangana India

ISBN: (纸本)9789811955525;9789811955501;9789811955495

In present-day life, quality of air is reducing day by day which leads to environmental issues and also damaging public health with harmful gases present in the air where the ecosystem is affected. The implementation of a cost-efficient system that controls the air pollutants in a large area is a challenging task. Therefore, this paper suggests a portable air quality measurement unit. Most of the harmful gases are released from industries and vehicles. Internet of things (IoT) plays an important role in the monitoring of air quality. We measure the quality of air using an mq135 sensor with also carbon oxide using mq7. Measuring the air quality is important for bringing awareness to the people for taking care of future generations for their health. Based upon the review, Government of India has taken some measures to ban single stroke and two-stroke engine motorcycles which are causing high pollution. Although New Delhi remarked as the most polluted city comparing with other cities in the world which as recorded air quality above 300 ppm. IoT is used for clouded computing where it is used for collecting data from different sensors. We are using artificial intelligence to predict the future data from the collection in the database. The data collected at the cloud application can be processed by using data analytics and artificial intelligence (AI). After knowing the future predicted data, we can be alert and take precautionary measures and keep the indoor air quality as good as possible. We have used the support vector machine algorithm which is popularly known as svm. It is also famously known as supervised machine learning. Satisfactory alternatives based on different conditions are generated by using the proposed model. The proposed method focuses on air pollutant control with IoT and AI in combination with the svm algorithm.

关键词： Raspberry Pi svm algorithm Cloud computing Air monitoring Artificial intelligence

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Intelligent Personalized Nutrition Guidance System Using IoT and Machine Learning algorithm 2

Intelligent Personalized Nutrition Guidance System Using IoT...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Santhuja, P. Choudri, Subramani Roy Balaji, S. Reddy, Elangovan Guruva Muthulekshmi, M. Department of Computer Science Engineering CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Information Technology Sri Shanmugha College of Engineering and Technology Tamil Nadu Salem India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Nowadays, dietary issues are increasing around the world. Numerous problems, such as weight gain, obesity, diabetes, etc., can arise from an unbalanced diet. By integrating image processing, the system can assess food images in new ways to provide personalized solutions for better eating habits. The system uses machine learning, IoT, and image processing techniques to evaluate food image data and extract useful information. IoT devices like smartphones or specialized cameras take images of food and send to the cloud for analysis. The Support Vector Machine (svm) is a machine learning technique that is integrated with Internet of Things (IoT) technology in this study to propose a novel method for creating a system that uses personalized nutritional suggestions. The svm algorithm is then used to analyze and analyze this data to find patterns, correlations, and individual dietary requirements. A cloud database is used to process and store all the data related to food consumption. The system analyzes food images and determines the nutrition and caloric content of the food using image processing and segmentation. The system takes up new abilities using an extensive collection of dietary records that have been labeled and include details about the sorts of foods, portion sizes, and nutritional value. The system can predict and identify users' nutritional needs, deficits, and personalized dietary objectives by using this trained svm model to their nutritional data. © 2023 IEEE.

关键词： Dietetics IoT Machine Learning Nutrition Guidance svm algorithm

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Research on Ocean Adaptation Zone Classification Prediction Model Based on Gravity Anomaly data 2

Research on Ocean Adaptation Zone Classification Prediction ...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： He, Chenkai He, Hongyi Chen, Yiru Dalian Polytechnic University School of Food Science and Technology Liaoning Dalian China Dalian Polytechnic University School of Biological Engineering Liaoning Dalian China

ISBN: (纸本)9798350317558

This paper aims to propel the development of the marine economy by proposing a crucial underwater navigation adaptation zone classification prediction technique. Firstly, through meticulous processing of gravity anomaly benchmark data, the maritime area is delineated, and adaptability is calibrated to ensure the navigation precision of underwater vehicles. Using the K-means classification model, the data is refined and categorized into zones of medium-high accuracy, low accuracy, medium accuracy, and high accuracy. Subsequently, specific feature attribute indicators are selected, and an effective regional adaptation zone classification prediction model system, denoted as F, is established to ensure navigation accuracy within regions with different gravity anomaly feature distributions. Finally, transferability predictions are performed on new gravity anomaly data to explore the model's applicability to novel data, validating the robustness and generalization capability of the model and fostering further advancements in underwater navigation technology. This research holds significant importance for driving the marine economy forward. © 2024 IEEE.

关键词： Data-driven interpolation processing K-means clustering algorithm Python svm algorithm

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Real-Time Video Management System for Robotic Waste Sorting and Recycling Using IoT and Machine Learning 2

Real-Time Video Management System for Robotic Waste Sorting ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Raman, Ramakrishnan Meenakshi, R. Latha, R. Rawandale, Chandrashekhar J. Jayaprakash, S. Srinivasan, C. Maharashtra Pune India Department of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Tamil Nadu Trichy India Symbiosis Law School Uttar Pradesh Noida India Maharashtra Pune India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

The system automated robotic for waste sorting and recycling by identifying and categorizing various forms of waste. Traditional sorting of waste takes time and is prone to human mistakes. This solution uses IoT and machine learning to overcome these restrictions and improve waste management efficiency. The system begins with waste-collecting area sensors and cameras. The sensors record waste item weight, size, and color, while the cameras record images. The Raspberry Pi, a small image-processing single-board computer, processes sensor and camera data. Raspberry Pi transfers processed data to the cloud for analysis and decision-making. The Support vector machine (svm) method classifies waste in the cloud using machine learning. A dataset of waste categories trains the svm algorithm to discriminate recyclable from non-recyclable materials. The system controls robotic arms or conveyor belts to segregate waste after classification. The robotic arms effectively classify garbage into containers. Real-time video management ensures fast, precise waste sorting, enhancing recycling efficiency. The system can gather data from many garbage-collecting regions concurrently. The cloud architecture enables real-time data processing and analysis. svm-based machine learning techniques improve waste categorization accuracy, improving trash sorting. Waste sorting and recycling using the real-time video management system is novel. IoT, machine learning, and cloud computing facilitate robotic waste segregation. The method might improve waste management and environmental conservation. Optimizing waste management operations, reducing manual labor, and promoting sustainable methods. © 2023 IEEE.

关键词： Cloud Internet of Things Recycling Sensors svm algorithm Waste Sorting

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Research on PSO-svm base wine grade recognition based on Max-Relevance and Min-Redundancy feature selection

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2024年第17期36卷 e8124-e8124页

作者： Yang, Jiaping Ren, Dongping Liu, Yong Zhou, Hailong Sun, Yunquan Fudan Univ Inst Engn & Appl Technol Shanghai Peoples R China Luzhou Laojiao Co Ltd Brewing Proc Optimizat Dept Luzhou Peoples R China Sichuan Univ Sci & Engn Sch Automat & Informat Engn Zigong Peoples R China Artificial Intelligence Key Lab Sichuan Prov Teaching & Res Sect Zigong Peoples R China Luzhou Zhitong Automat Equipment Co Ltd Res & Dev Dept Luzhou Peoples R China

In response to the challenges inherent in Baijiu classification, characterized by ambiguity and limited methodologies, this study introduces a novel framework for comprehensive Baijiu brewing process management. By integrating mathematical models and machine learning algorithms, our aim is to standardize and enhance the accuracy of the Baijiu brewing process. Through a meticulous analysis of 13 input features using the MRMR algorithm, we identified key variables that significantly influence base wine grades. Subsequently, we compared the efficacy of support vector machine (svm) algorithms under three kernel functions for base wine grading classification. Additionally, we developed an enhanced particle swarm-support vector machine (IPSO-svm) algorithm, which incorporates nonlinearly decreasing inertia weights to enhance global and local search capabilities. Evaluation metrics, including accuracy and F1-SCORE, were employed to assess the model's performance. Our experimental results demonstrate that IPSO-svm significantly improves convergence speed and accuracy, achieving a classification accuracy of 98.31% on the test set with an F1-SCORE of 98.18%. Furthermore, experimental tests conducted at a prominent strong-flavored wine enterprise in China validated the model's effectiveness, revealing no significant difference in grading quality between model and manual Baijiu classification, with a mean error below 1%. This study presents a pioneering approach to digitize and standardize Baijiu classification processes, laying theoretical foundations for the development of intelligent Baijiu classification systems.

关键词： Baijiu classification process feature selection IPSO-svm algorithm svm algorithm

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Comprehensive analysis method model of Aerobics performance based on Artificial Intelligence 8

Comprehensive analysis method model of Aerobics performance ...

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8th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Lv, Li Ye, Chunjuan Tianjin Univ Technol & Educ Dept Phys Educ Tianjin 300222 Peoples R China

ISBN: (纸本)9781509023134

Aerobics is a new sport in our country. Because of different ethnic groups, there are differentiated competition. How to carry on the quantitative research to our country youth special skill is a major task to improve our athletes comprehensive quality and competitive ability of athletes. In this paper, based on the modern intelligent algorithm, the high precision classification performance of support vector machine is combined with the robustness of the genetic algorithm. In this paper, the young athletes as the research object, quantitative research on the physical fitness index, and establish the quantitative model of the intelligent evaluation of adolescent aerobics skills

关键词： svm algorithm genetic algorithm space design Aerobics

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Near-Infrared Raman Spectroscopy for Detection of Gastric Cancer Peritoneal Dissemination in vivo

Near-Infrared Raman Spectroscopy for Detection of Gastric Ca...

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Conference on Clinical and Biomedical Spectroscopy and Imaging II

作者： Ma Jun Mao Wei-zheng Xu Ming Gong Long-jing Gao Yuan Zhou Han-jing Zheng Rong-er Ocean Univ China Opt & Optoelect Lab Qingdao 266100 Peoples R China Qingdao Municipal Hosp Dept Gen Surg Qingdao 266003 Peoples R China

ISBN: (纸本)9780819486844

The nude mice injected with human gastric cancer cells (SGC-7901) in their peritoneums were chosen as the animal models of gastric cancer peritoneal dissemination in this research. The Raman spectra at 785nm excitation of both these nude mice which were in different tumor planting periods and the normal counterpart were taken in vivo in the imitate laparotomy. 205 spectra were collected. The spectra of different tissue types were compared and classified by Support Vector Machine (svm) algorithm. Significant differences were showed between normal and malignant tissues. The gastric cancer nodules had lower Raman intensities at 870, 1330, 1450, and 1660cm(-1), but higher at 1007, 1050, 1093 and 1209cm(-1), compared with normal tissues. Additionally, the spectra of malignant tissues had two peaks around 1330 cm(-1) (1297cm(-1) and 1331cm(-1)), while the spectra of normal tissues had only one peak (1297cm(-1)). The differences were attributed to the intensities of the stretching bands of the nucleic acid, protein and water. These features could be used to diagnose gastric cancer. The Support Vector Machine (svm) algorithm was used to classify these spectra. For normal and malignant tissues, the sensitivity, specificity and accuracy were 95.73%, 70.73% and 90.73%, respectively, while for different tumor planting periods, they were 98.82%, 98.73% and 98.78%. The experimental results show that Raman spectra differ significantly between cancerous and normal gastric tissues, which provides the experimental basis for the diagnosis of gastric cancer by Raman spectroscopy technology. And svm algorithm can give the well generalized classification performance for the samples, which expands the application of mathematical algorithms in the classification.

关键词： Laser Roman Spectroscopy In vivo Nude mice Gastric cancer svm algorithm

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A Comparative Analysis of Machine Learning Techniques for Breast Cancer Prediction 2

A Comparative Analysis of Machine Learning Techniques for Br...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Dutta, Umesh Kaushik, Simran Iyer, Srinidhi Singh, Ina Manav Rachna International Institute of Research and Studies Department of Electronics and Communication Engineering Haryana Faridabad India Manav Rachna International Institute of Research and Studies Manav Rachna Innovation & Incubation Centre Haryana Faridabad India

ISBN: (纸本)9798350327533

One of the major diseases found in women worldwide is Breast Cancer. More than 2.2M cases have been reported with about a 30% mortality rate in accordance with WHO. The rapid advancement of technology has led to breakthroughs in different fields such as medicine, automotive, etc. Despite advancements in screening technologies, achieving high sensitivity and specificity for early detection remains a challenge. Machine Learning is one of the front runners and is currently playing a vital role in solving real-world problems, particularly in the healthcare domain. Using different Machine Learning algorithms prediction of Breast Cancer has been tallied in this work. Various algorithms such as svm (Support Vector Machine), KNN (K-Nearest Neighbours), DT (Decision Tree), and RF (Random Forest) have vast application areas. In this study, these algorithms have been tested and evaluated on the basis of precision, recall, and accuracy which are the key performance metrics for the evaluation of the model. The dataset for breast Cancer has been pulled from Kaggle, which is the most prominently used dataset by researchers. The simulation results revealed that Random Forest outperformed the others with the precision of 94.5%, recall of 94%, and accuracy of 95%, clearly showing its potential to be used for early prediction of Breast Cancer. © 2024 IEEE.

关键词： Breast Cancer Prediction DT algorithm KNN algorithm Modelling using ML RF algorithm svm algorithm

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