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Breast Cancer Detection in Mammogram Images using machine learning Methods and CLAHE Algorithm 5

Breast Cancer Detection in Mammogram Images using Machine Le...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Srivastav, Gaurav Rashid, Mamoon Singh, Richa Gehlot, Anita Sharma, Neha Computer Science & Engineering Datta Meghe Institute of Higher Education & Research Maharastra India Vishwakarma University Faculty of Science & Technology Department of Computer Engineering Pune India Computer Science & Engineering Krishna Institute of Engineering & Technology Gaziabad India Uttaranchal University Uttaranchal Institute of Technology Uttrakhand Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350398267

Breast cancer is one of the most common cancer types. this is the second-leading cause of cancer-related death in women. It ranked 2nd according to available data lung cancers is the only one causing more causalities. It's critical to receive a breast cancer diagnosis quickly. the MIAS data set is used in this study to examine machine learning-based categorization approaches used to study breast cancer. Important image data is fetched from mammograms. We choose eight distinct classifiers and assess each one's precision, recall, accuracy, and F-score. the analysis's findings were higher than 69.88%. © 2022 IEEE.

关键词： machine learning

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IoT and machine learning Based Food Recognition System 5

IoT and Machine Learning Based Food Recognition System

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Soewu, Taiwo Gupta, Jatin Kumar, Sumit Shruti Kaur, Baljinder Olabisi Onabanjo University Department of Computer Engineering Ago Iwoye Nigeria Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India Lovely Professional University School of Computer Science and Engineering Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798350398267

In a world where people are busy with job life, people who stay away from family and live in far cities, who have no one to go back home to, it is difficult to balance home and work life. Here the social cause we are working on is about how people won't know what fruits or vegetables are present at home and they have to travel back home just to check which will cowhide h lot of time and energy. In this study, with the help of IoT and machine learning, we will study how to information about what is there in our fridge or anywhere where food is kept to our phones via the cloud. machine learning is used here to detect what and how many vegetables or fruits are there using the supervised algorithm cal the led the KNN algorithm. IoT is deployed here to retrieve information from the room cloud and send it to our phones using the Blynk app. © 2022 IEEE.

关键词： Fruits

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IoT and Cloud Based health monitoring system Using machine learning 5

IoT and Cloud Based health monitoring system Using Machine l...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Preeti Rana, Chhavi Maharishi Dayanand University Computer Science and Engineering Haryana Rohtak India

ISBN: (纸本)9798350398267

the health care sector is focusing on in-home health care services, where the patients can receive medical care in the privacy of their own home. A patient in a rural region can use a remote health monitoring system to communicate with a doctor in a city who is in a larger city. machine learning has been used for smart health monitoring systems. they used a wearable sensor to identify a set of five parameters, including Electrocardiogram (ECG), pulse rate, pressure, temperature, and position detection. the technology uses machine learning algorithms to identify doctors for consultation and to identify and predict ailments. In the study, IoT technology and health monitoring have been coupled to give more personalized and responsive health care. the primary purpose of the system is to monitor patients' vital signs in real-time monitoring. the authorized individual can access the patient' s vital signs from their smartphone or PC using a cloud server. the Decision Tree (DT) attained the best accuracy of 99.1 percent after testing the suggested model, which is promising for their purposes. It is observed that the DT achieves best accuracy, while Random Forest is the second-best classifier for this problem. © 2022 IEEE.

关键词： Electrocardiograms

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5th IEEE conference on Dependable and Secure computing, DSC 2022 and SECSOC 2022 Workshop, PASS4IoT 2022 Workshop SICSA International Paper/Poster Competition in Cybersecurity

5th IEEE Conference on Dependable and Secure Computing, DSC ...

引用

5th IEEE conference on Dependable and Secure computing, DSC 2022

ISBN: (纸本)9781665421416

the proceedings contain 45 papers. the topics discussed include: show me your attach request and I'll tell you who you are: practical fingerprinting attacks in 4G and 5G mobile networks;a scary peek into the future: advanced persistent threats in emerging computing environments;reliability models and analysis for triple-model with triple-input machine learning systems;security orchestration, automation, and response engine for deployment of behavioral honeypots;a co-evolutionary algorithm-based malware adversarial sample generation method;securing password authentication for web-based applications;graph neural network-based android malware classification with jumping knowledge;multi-task learning model based on multiple characteristics and multiple interests for CTR prediction;device-to-device task offloading in a stochastic invalid-device scenario with social awareness;and a novel approach for providing client-verifiable and efficient access to private smart contracts.

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Traffic Flow Prediction Using Deep learning Models 5

Traffic Flow Prediction Using Deep Learning Models

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5th International conference on computing and Big Data, ICCBD 2022

作者： Wu, China-Seh Manthena, Satya Pranavi Kale, Saurabh Kulkarni, Maithili Vinayak San Jose State University Department of Computer Science San JoseCA United States

ISBN: (纸本)9781665457163

For the effective implementation of Intelligent Transportation Systems (ITS), accurate and timely traffic flow information is critical. Traffic data has exploded in recent years, ushering in the era of big data. the main issue of a traffic flow prediction system is determining how to build an adaptive model based on past data. Existing traffic flow forecast approaches rely on shallow learning models, which are unsatisfactory for many real-world applications. this situation prompts us to reconsider traffic flow prediction using deep learning models and large amounts of traffic data. In this paper, we present the Long Short-Term Memory (LSTM) model, Gated Recurrent Unit (GRU) model, Stacked Auto Encoder (SAE), and an Auto-Regressive Integrated Moving Average (ARIMA) model, all of which are deep learning methods. these techniques are applied to real-world traffic big data collected by performance measurement systems. Compared to other deep learning and shallow machine learning prediction networks, experimental results demonstrate that the gated recurrent unit model is more applicable and performs better. © 2022 IEEE.

关键词： Forecasting

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Application of Artificial Neural Network on South African Sign Language Recognition System 5

Application of Artificial Neural Network on South African Si...

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5th International conference on Artificial Intelligence, Big Data, computing and Data Communication Systems (ICABCD)

作者： Ndlovu, Wonder Alatise, Mary Tshwane Univ Technol Comp Syst Engn Dept City Of Tshwane South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Sign language assumes a significant part in filling the oral communication void for non-hearing or hard of hearing people. Despite the use of sign language, most people might not understand sign language clearly. Globally, research has been ongoing to explore and recommend various sign language recognition systems for different sign languages. On the other hand, researchers are still faced with challenges of practically implementing recognition systems. though, machine learning has emerged as one of the advanced sign language recognition systems. Artificial neural network (ANN) models and techniques are machine learning models which are widely utilised to improve the accuracy level thus offering better results when recognising sign language gestures. However, there are few or no actively existing South African Sign Language (SASL) automatic recognition systems readily available to facilitate communication seamlessly and instantly between non-hearing and hearing people. this research concluded that application of finger tracking technique significantly improved feature extraction, training and prediction of hand gestures produced under different conditions, i.e. skin color and background. While the application of artificial neural network on SASL recognition system led to better performance with 98.77% and 97.5% accuracy scores during training and prediction, respectively. In this paper, the proposed ANN utilised finger tracking techniques and forward and backward algorithms to implement a system that recognizes fingerspelling in SASL.

关键词： SASL Finger Tracking machine learning

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5th International conference on computing and Informatics, ICCI 2022

5th International Conference on Computing and Informatics, I...

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5th International conference on computing and Informatics, ICCI 2022

ISBN: (纸本)9781665499729

the proceedings contain 64 papers. the topics discussed include: real-time fixed-point hardware accelerator of convolutional neural network on FPGA based;dependable cnc controller using raspberry pi and cloud computing;smart potato disorders diagnostic system based on fuzzy k-nearest neighbor;review of personalized cancer treatment with machine learning;image processing-based vehicle class identification in mixed traffic;image cryptography: a systematic review;why bee colony is the most suitable with multi-query optimization?;gamification tool design for reducing requirements ambiguity during elicitation;new graphical ultimate processor for mapping relational database to resource description framework;group of models of error flow sources for discrete q-ary channels;predicting banking customer churn based on artificial neural network;and a framework for enhancing the supply of scientific research projects using blockchain.

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Clustering-Based Network Intrusion Detection System 5

Clustering-Based Network Intrusion Detection System

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5th IEEE conference on Dependable and Secure computing (IEEE DSC)

作者： Fan, Chun-I Lai, Yen-Lin Shie, Cheng-Han Natl Sun Yat Sen Univ Dept Comp Sci & Engn Kaohsiung Taiwan

ISBN: (数字)9781665421416

ISBN: (纸本)9781665421416

the increasing sophistication of network attacks and the inability of traditional defensive techniques such as firewalls or weak passwords against them have led researchers to propose network intrusion detection systems. Many network intrusion detection systems using machine learning techniques have been proposed, but the detection performance of some systems can be further improved. In addition, many systems adopted multiple machine learning classifiers to cooperate in generating detection results, but the individual classifiers in the system are often difficult to operate independently, limiting the flexibility of the system. this paper presents a Clustering-Based Network Intrusion Detection System, which applies the concept of clustering to detect network attacks by using the K-Nearest Neighbor algorithm for the initial detection of network attack types, and the Decision Tree algorithm specializes in detecting specific types of attacks. this improves the detection performance of the system and maintains the usability of an individual classifier.

关键词： Intrusion Detection System machine learning Clustering K-Nearest Neighbor Algorithm Decision Tree Algorithm

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Correlative Exposition of Various machine learning Techniques for Urdu Handwritten Text Recognition 5th

Correlative Exposition of Various Machine Learning Technique...

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5th International conference on Recent Innovations in computing, ICRIC 2022

作者： Rashid, Dhuha Gondhi, Naveen Kumar Chaahat Shri Mata Vaishno Devi University J&K Katra India Model Institute of Engineering and Technology J&K Jammu India

ISBN: (纸本)9789811998751

One of the most under-researched area in the field of natural language processing (NLP) has been the text classification. Text classification of languages like Urdu is more deprived of research since it is not among the top-most spoken language of the world. Despite that, almost 230 million people around the world speak Urdu. Researchers have worked with various machine learning and deep learning models for the identification of handwritten text of Urdu language. this paper is the comparative analysis of the various algorithms that have been used on the offline as well as online text recognition of Urdu language. From our analysis, CNN is the most widely adopted model for recognition of Urdu handwritten text due to its excellent properties of image classification as well as feature extraction. this paper gives a brief of various machine learning algorithms used for recognition of Urdu text and their comparative analysis. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Character recognition

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Improved machine learning Algorithms for Fraud Detection in Fintech Companies 5th

Improved Machine Learning Algorithms for Fraud Detection in ...

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5th International conference on Financial Technology-ICFT

作者： Nwachukwu, Chukwuemeka Akwiwu-Uzoma, Chukwuebuka Ovuehor, Samuel Durodola-Tunde, Kehinde Univ Bradford Dept Appl AI & Data Analyt Bradford W Yorkshire England Univ Dundee Dept Comp Dundee Scotland

ISBN: (纸本)9789819638109;9789819638116

the rapid adoption of mobile money is due to the penetration of smart devices and the reduced cost of computing power. Today, many rural dwellers use mobile smart devices (Ofcom, 2022). A financial solution that creates a transaction system through mobile smart devices is instrumental in closing the financial inclusion gap. Mobile money offers digital financial service providers access to an untapped market that is profitable. there has been a shift from traditional banking to digital banking services (mobile money). this is due to a rapid increase in mobile phone penetration and reliance on domestic remittances in many households (Mauree and Kohli, 2013). With the broad internet usage, modern technologies, and the current growth of digital financial service providers, there is a need for financial institutions to mitigate against financial crimes and fraudulent activities (Wewege et al., 2020). the sporadic expansion of digital transformation has made digital financial services providers vulnerable to cyber risks. the continuous partnerships with third parties, technology providers and fintech companies have heightened their vulnerability to cyber-attacks (Tsys, 2017). More people are prioritizing their delicate personal data against hacking, identity theft, cyberattacks and money laundering. Digital banks with lesser security infrastructure and small fintech companies are more prone to financial fraud and cyber risks. Hence, digital financial service providers and fintech companies should continuously update their security infrastructure (Wewege et al., 2020). this study focuses on how fintech companies can maximize improved machine learning algorithms for fraud detection.

关键词： machine learning Mobile Money Fraud Detection

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