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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Shiv Ashish Dhondiyal Pooja Luthra Bhupender Sharma Rajni Rajni Ishita Uniyal Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Chandigarh University Mohali Punjab Department of Computer Science and Application Coer University Roorkee Uttarakhand Department Of Computer Science & Engineering Guru Jambheshwar University of Science and Technology Hisar Haryana India Department of Electrical Engineering Graphic Era Hill University

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer.

关键词： Image segmentation Accuracy Machine learning algorithms Sensitivity Databases Nearest neighbor methods Prediction algorithms Breast cancer Classification algorithms Random forests

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Method for Judging Copyright Infringement of Illegally Distributed Streaming Videos Using Cloud Services

Method for Judging Copyright Infringement of Illegally Distr...

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2023 International Conference on Artificial Intelligence and Power engineering, AIPE 2023

作者： Jnag, Seyoung Park, Byeongchan Yoo, Injae Lee, Jaechung Kim, Seok-Yoon Kim, Youngmo Soongsil University Dept. of Computer Science and Engineering Seoul Korea Republic of BeyondTech Inc. Institute of Technology Seoul Korea Republic of

ISBN: (纸本)9798350310788

Recently, copyright infringement crimes avoid crackdowns and investigations in various ways, such as operating servers overseas or using cloud services and disguising the IP of the server. Therefore, appropriate countermeasures are needed. In this paper, we propose a method for determining illegal distribution of copyright-infringing streaming videos using cloud services that quickly determines whether an illegal digital content is being served. Using this method, there is an advantage in that copyright infringing content sites using cloud services can be quickly found and blocked. © 2023 IEEE.

关键词： Copyrights

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Artificial Neural Network-Based Assessment of Two 20Kw PV Solar Energy Generation Plants in Homogeneous Environments 3

Artificial Neural Network-Based Assessment of Two 20Kw PV So...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Ghildiyal, Shivangi Mittal, Amit Prashant, G.C. Soni, Mukesh Raparthi, Mohan Bhat, Niyati Invertis University Dept of Computer Applications Bareilly India Graphic Era Hill University Dept of Allied Sciences Bhimtal India Texas Tech University Department of Computer Science Lubbock79409 United States Dr. D.Y. Patil Vidyapeeth Pune Dr. D.Y. Patil School of Science &technology Tathawade Pune India Alphabet Life Science DallasTX75063 United States University of Mumbai Computer Engineering Maharashtra Mumbai400032 India

ISBN: (纸本)9798350381931

Renewable energy initiatives are of paramount importance in the substantial transformation of conventional power generation. An opportunity presents itself to mitigate the escalating demand for electricity through the utilization of the plentiful and intense solar radiation. The global influence of industrialization has led to a significant reliance on solar energy as a renewable source to meet India's domestic energy needs. In the Indian state of Uttarakhand, the purpose of this article is to provide an objective evaluation of the many solar photovoltaic (PV) power facilities that are now in operation. Photovoltaic (PV) systems harness solar energy to provide clean electricity, free from pollution or noise. Photovoltaic (PV) systems are characterized by their simplicity, ease of installation, and infrequent occurrence of malfunctions. Regrettably, the amount of energy produced by photovoltaic (PV) systems is contingent upon weather conditions, geographical location, and the design of the system. Several parameters are examined to evaluate technically identical solar power plants. Photovoltaic module tilt angle, annual deterioration, negative temperature coefficient of power, and other factors are considered. The assessment also includes identifying the reasons for any deviation in performance. © 2024 IEEE.

关键词： Solar energy

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Enhancing Indoor Positioning of Wireless Access Points using RSSI Fingerprints

Enhancing Indoor Positioning of Wireless Access Points using...

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IEEE International Conference on Electro-Information Technology

作者： Ahlam Al Mohammad Daniel Marku John Reedy Stephen Reedy Mina Maleki Shadi Banitaan Dept. of Electrical & Computer Engineering & Computer Science University of Detroit Mercy Detroit MI United States

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

Location-based services in commercial and industrial settings enhance power efficiency and security by providing real-time visibility of people within designated areas. Indoor facilities, in particular, benefit from accurate indoor localization to optimize resources and ensure a safe environment. This study presents an approach to localizing wireless access points (APs) within indoor environments using a handheld smartphone. The method leverages received signal strength indicator (RSSI) scans from APs via fingerprints and employs a customized localization algorithm. Three localization algorithms—filtration, non-linear, and linear trilateration—are explored to determine the most effective means of AP estimation within commercial buildings. The filtration process includes discarding the least reliable RSSI scans, identifying the predominant floor number, and enhancing accuracy through a weighted average computation using RSSI values. Meanwhile, the trilateral linear and non-linear approaches utilize a multilateral localization algorithm to estimate the location of an AP. Results indicate that the filtration approach yields the closest estimates to the AP’s real location, with an average of 2.4 meters or 8 feet away. In contrast, non-linear trilateration is more consistently close to the AP but with a noticeably further average prediction margin, while linear trilateration exhibits skewed results due to extreme outliers. Filtration is thus the preferable method as long as sufficient data is collected for accurate estimation. In conclusion, given the available data, the filtration approach emerges as the most suitable method for AP estimation in commercial buildings, producing estimates that are heavily weighted towards the closest possible AP location. However, ensuring adequate data collection for accurate localization results is essential.

关键词： Location awareness Meters Accuracy Filtration Buildings Estimation Fingerprint recognition

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Data Visualization for Corona Patients Globally using Real-Time APIs 2

Data Visualization for Corona Patients Globally using Real-T...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Durai, S. Iqbal, M. Mohamed Kumar, S Niresh Alagappan, Chockalingam Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Dept. of Cse Chennai India VIT-AP University School of Computer Science and Engineering Amaravati India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India M.Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Tamilnadu Karur639113 India

ISBN: (纸本)9781665491990

All the nations' administrative units are concerned about the COVID-19 outbreak in different regions of the world. India is also trying to control the spread of the virus with strict measures and has managed to slow down its growth rate. The administration of each country faces the challenge of maintaining records of corona patients. To address this challenge, this work builds a website from scratch using real-time APIs for data visualization. The website provides information on the number of active cases, death cases, recovery cases, and total cases of COVID-19 patients in each country. The data can be visualized using graphs, making it easier to compare the situation in different countries. The website allows for monitoring which country has a higher number of deaths, patients, favorable recovery rates, and a large number of active cases. The COVID-19 status regarding patients can be analyzed through graphical representation using real-time data. © 2023 IEEE.

关键词： COVID-19

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An Experimental Study on No-Reference Image Quality Assessment of Image Datasets 7

An Experimental Study on No-Reference Image Quality Assessme...

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7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Magotra, Varun Rohil, Mukesh Kumar Bhartiya Vidya Bhavans Sardar Patel Institute of Technology Dept. of Computer Engineering Mumbai India Birla Institute of Technology and Science Dept. of Computer Science Rajasthan Pilani India

ISBN: (纸本)9781665421683

Image quality assessment algorithms are of various types depending upon the the method they are employing. Over the years, these algorithms have found various applications like image compression, image restoration, image transmission etc. Image quality assessment algorithms have played a significant in these fields by appropriately evaluating the quality of the images. Based on the structure of these algorithms, in this paper, we further explored the application of Image Quality Assessment algorithm especially Non-Reference Algorithm in the world of AI. In this paper, we have showed how the Non-reference IQA algorithms can be used to assess the quality of the images in the data that are being used in for training the deep learning models. In our study, we specially focused on the datasets that are being used in the training of the deep learning models that are used in the healthcare sector. We have showed how non-reference algorithms can be used to create a more quality data so that the efficiency of the automated systems which heavily relies on the data quality can be increased. © 2022 IEEE.

关键词： Image compression

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Predictive Modeling of Metastatic Diagnosis Period in Breast Cancer using Machine Learning

Predictive Modeling of Metastatic Diagnosis Period in Breast...

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engineering Informatics, IEEE

作者： Aisha Karigar Mohammed Qadir Ternikar Samrudhi Patil Priyanka Gavade dept.of Computer Science (of Aff.) KLE TECHNOLOGICAL UNIVERSITY DR.MSSCET CAMPUS (of Aff.) BELGAVI INDIA

ISBN: (数字)9798331505776

ISBN: (纸本)9798331505783

The objective of this research is to utilize machine learning models to forecast the period of metastatic diagnosis in breast cancer patients, with a specific focus on triple-negative breast cancer (TNBC) data sourced from the WiDS Datathon 2024 dataset, which includes 19,000 records. This dataset encompasses a wide range of medical and socioeconomic variables that play a crucial role in comprehending healthcare disparities. Our assessment encompassed models such as Random Forest, XGBoost, and Support Vector Regression, with the Gradient Boosting Regressor (GBR) emerging as the most precise, achieving a Root Mean Square Error (RMSE) of 80.685. An analysis of feature importance revealed that the breast cancer diagnosis code, patient age, and geographic location (zip3) were the most significant predictors. Although feature importance was lower, payer type and socioeconomic factors also contributed to the model’s predictive capabilities. This study underscores the capacity of machine learning to capture both major and minor influences on metastatic diagnosis, thereby assisting in early detection and enhancing treatment strategies for TNBC patients.

关键词： Support vector machines Medical services Predictive models Breast cancer Data models Socioeconomics Root mean square Informatics Random forests Medical diagnostic imaging

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Using Machine Learning and Google Earth Engine to Understand Land Use and Land Cover Classifications and NO2 Levels in California

Using Machine Learning and Google Earth Engine to Understand...

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IEEE International Conference on Electro-Information Technology

作者： Benyamain Yacoob Ethan Scheys Eyiara Oladipo Andre Price Shadi Banitaan Dept. of Electrical & Computer Engineering & Computer Science University of Detroit Mercy Detroit MI United States

ISBN: (数字)9798350330649

ISBN: (纸本)9798350330656

Anthropogenic activities release pollutants into the air, which can negatively affect human health and the environment. One such pollutant is nitrogen dioxide (NO 2 ), which can contribute to smog formation, decreased crop growth and yield, and respiratory damage. This study aimed to find a relationship between land use/land cover (LULC) classifications and NO 2 levels in the air. We used Google Earth Engine (GEE) to collect LULC and air quality data using the Google Dynamic World and the Sentinel-5P NRTI NO 2 datasets. We focused on Pasadena, California, as it provided a good demonstration of an urban area surrounded by greenery, allowing for an adequate analysis of both forms of landscape and their impact on air quality. Random forest (RF) and decision tree (DT) classifiers were used on the provided datasets, with the estimated probability of complete coverage for each LULC type being the input features and the NO 2 density being the output label, measured in mol/m 2 . Our output labels were then discretized, classifying the categories into high and low NO 2 . The machine learning classifier found a correlative relationship between LULC and NO 2 levels, as signified by our modeled accuracy outputting a value of 85%, with an average f1 score of 86%. We performed 10-fold cross-validation to enhance the reliability of model evaluation. The results from this study suggest that machine learning models can be used to predict the changes in air quality based on changes in LULC from anthropogenic activities. With future studies confirming this relationship, inner-city green spaces may benefit mental and physical well-being.

关键词： Earth Atmospheric modeling Green products Urban areas Predictive models Air quality Internet

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A Novel Analysis and Detection of Autism Spectrum Disorder in Artificial Intelligence Using Hybrid Machine Learning

A Novel Analysis and Detection of Autism Spectrum Disorder i...

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Senthil, G.A. Prabha, R. Nithyashri, J. Suganthi, P. Thamarai, I. Sridevi, S. Agni College of Technology Department of Information Technology Chennai India Sri Sai Ram Institute of Technology Department of Electronics and Communication Engineering Chennai India School of Computing SRM Institute of Science and Technology Kattankulathur Dept. of Computing Technologies Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai City Campus Chennai India Sri Ramachandra Engineering and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397208

Heart Disease or Cardiovascular Disease refers to the range of heart conditions like cardiac arrest, coronary artery disease. Heart disease can be very well hindered through certain lifestyle changes. There is a significant increase in the mortality rate recently due to the distinctive heart diseases. Machine learning uses mathematical models to work efficiently with the enormous amount of data. It plays a crucial role in medical science in the prediction of distinctive diseases. Cardiologists inspects the heart functionality using electrocardiography, computed tomography. These tests are quite expensive for a common man. Recent times, the life span of a human is guaranteed only with the support of medications. As prevention is better than the cure, machine learning helps to predict the vulnerability of a heart disease with few elemental symptoms and health factors. It is been fed by the basic data of the patients like age and sex. Machine learning helps to predict the vulnerability in advance which provides the cardiologists with great acumen for the adaption of the treatment. Machine learning algorithms have proven to produce reliable and accurate output with the help of the inputs. The algorithms used in the article include K-Nearest Neighbour (KNN) and decision tree classifier which is compared to yield the desired and efficient output. © 2023 IEEE.

关键词： Heart

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A Fast and More Accurate Seed-and-Extension Density-Based Clustering Algorithm

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IEEE Transactions on Knowledge and Data engineering 2023年第6期35卷 5458-5471页

作者： Tung, Ming-Hao Chen, Yi-Ping Phoebe Liu, Chen-Yu Liao, Chung-Shou Research and Development Micron Technology Inc Hsinchu Taiwan Department of Computer Science and Information Technology La Trobe University Melbourne Australia Dept. Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu Taiwan Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu Taiwan

Clustering algorithms have been widely studied in many scientific areas, such as data mining, knowledge discovery, bioinformatics and machine learning. A density-based clustering algorithm, called density peaks (DP), which was proposed by Rodriguez and Laio, outperforms almost all other approaches. Although the DP algorithm performs well in many cases, there is still room for improvement in the precision of its output clusters as well as the quality of the selected centers. In this study, we propose a more accurate clustering algorithm, seed-and-extension-based density peaks (SDP). SDP selects the centers that hold the features of their clusters while building a spanning forest, and meanwhile, constructs the output clusters in a seed-and-extension manner. Experiment results demonstrate the effectiveness of SDP, especially when dealing with clusters with relatively high densities. Precisely, we show that SDP is more accurate than the DP algorithm as well as other state-of-the-art clustering approaches concerning the quality of both output clusters and cluster centers while maintaining similar running time of the DP algorithm, particularly for a variety of time-series data. Moreover, SDP outperforms DP in the dynamic model in which data point insertion and deletion are allowed. From a practical perspective, the proposed SDP algorithm is obviously helpful to many application problems. © 1989-2012 IEEE.

关键词： Heuristic algorithms

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