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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Rajesh, N. Alzubaidi, Laith H. Shivaprasad Yadav, S.G. Chembian, W.T. Prameela, Boddepalli Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India The Islamic University of Babylon Department of Computers Techniques Babylon Iraq Ramaiah Institute of Technology Department of Electronics and Telecommunication Engineering Bengaluru India Department of Computer Science and Engineering Chennai India Vignan's Institute of Information and Technology Department of Artificial Intelligence and Data Science Andhra Pradesh India

ISBN: (纸本)9798350352931

Road Extraction from High-Resolution Satellite Images (HRSI) plays a significant role in the analysis of the remote sensor image field. The extraction of road automatically labels a road's pixels in a satellite image with semantic features depending on topographical meaningful features. However, manual road extraction inaccurately extracts road networks from satellite images which has significant difficulties like complex urban environments, varying road types, and diverse image conditions. Therefore, the Scale Invariant Feature Transform-based Random Forest(SIFT-RF) is proposed for automatic road extraction in satellite images. First, the image is obtained from DeepGlobe dataset and Z-score normalization is employed to maximize model performance. Then, SIFT is established to extract the road network in satellite images. Finally, the RF is utilized to extract and classify roads as road or non-road using Machine Learning (ML). The SIFT-RF achieves a greater f1-score of 98.24% compared to existing methods like Dual-Attention Road extraction Network (DA-RoadNet), High-Resolution U-Net architecture (HRU-Net), and Global Context-aware and Batch-independent Network (GCBNet) respectively. © 2024 IEEE.

关键词： Random forests

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Elevating Maternal Healthcare: Synergy of Cardiotocography, Machine Learning Models and Interpretive Analysis

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Procedia computer science 2025年 258卷 2787-2797页

作者： G Niveditha B Uma Maheswari Rocío Pérez de Prado Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India Telecommunication Engineering Department. University of Jaén. Spain

The global goal of reducing maternal mortality and improving neonatal health, in particular during labor where clinical actions must be precise and timely, is important. Abnormalities such as low or high maternal oxygen levels, inappropriate uterine contraction patterns, or abnormal fetal heart rates during delivery can endanger the well-being of the mother and child. Currently, many essential signs are primarily assessed and recorded using visual means, which are susceptible to delay or even error. However, of late, machine learning has been characterized as a cutting-edge approach that helps improve practice in clinical settings by generating more accurate forecasts based on intricate data patterns This study’s objective is to formulate an appropriate machine-learning model for predicting maternal and fetal health risks at delivery for improved clinical outcomes. Within the freely accessible cardiotocography dataset consisting of the number of uterine contractions more than and the fetal heart rate of 21 features, Recursive feature elimination (RFE) folds the features down into 15. To solve the problem of the class imbalance MSMOTE (Modified Synthetic Minority Over-sampling Technique) is used for this purpose. A number of the machine learning procedures such as Random Forest, LightGBM, CatBoost, AdaBoost, Decision Tree, K-Nearest Neighbor, Support Vector Machine, Logistic Regression, Multi-Layer Perceptron, and Gradient Boosting amongst others are also trained and subjected to evaluation. Of these, CatBoost and LightGBM models recorded the highest level of accuracy at 96.0% and 95.1% respectively. To further interpret as well as validate the model predictions, LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations) used.

关键词： Fetal Health Machine learning Cardiotocography Recursive Feature Elimination MSMOTE LIME SHAP

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Environment Adaptive Deep Learning Classification System Based on One-shot Guidance

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computers, Materials & Continua 2022年第12期73卷 5185-5196页

作者： Guanghao Jin Chunmei Pei Na Zhao Hengguang Li Qingzeng Song Jing Yu School of Telecommunication Engineering Beijing PolytechnicBeijing100176China Department of Mathematics Wayne State UniversityDetroit48202MIUSA School of Computer Science and Technology Tiangong UniversityTianjin300387China

When utilizing the deep learning models in some real applications,the distribution of the labels in the environment can be used to increase the ***,to compute this distribution,there should be the validation set that is labeled by the ground *** the other side,the dependency of ground truths limits the utilization of the distribution in various *** this paper,we carried out a novel system for the deep learning-based classification to solve this ***,our system only uses one validation set with ground truths to compute some hyper parameters,which is named as one-shot ***,in an environment,our system builds the validation set and labels this by the prediction results,which does not need any guidance by the ground ***,the computed distribution of labels by the validation set selectively cooperates with the probability of labels by the output of models,which is to increase the accuracy of predict results on testing *** selected six popular deep learning models on three real datasets for the *** experimental results show that our system can achieve higher accuracy than state-of-art methods while reducing the dependency of labeled validation set.

关键词： Deep learning classification distribution of labels probability of labels

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Mammogram Breast Cancer Classification Based on Deep-Convolutional Neural Network

Mammogram Breast Cancer Classification Based on Deep-Convolu...

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2023 International Seminar on Application for Technology of Information and Communication, iSemantic 2023

作者： Amadea, Avissa Aurellia Sari, Christy Atika Rachmawanto, Eko Hari Sari, Wellia Shinta Noori, Mohammed S. Alkhafaji, Mohamed Ayad University of Dian Nuswantoro Study Program in Informatics Engineering Semarang Indonesia University of Dian Nuswantoro Study Program in Information Systems Semarang Indonesia Northern Technical University Department of Computer Engineering Technology Mosul Iraq National University of Science and Technology Department of Computer Science DhiQar Iraq

ISBN: (纸本)9798350339215

Based on WHO's data, breast cancer is one of the most deadly diseases that has claimed many victims, especially women. This disease begins with the presence of an undetected and eventually turns into malignant (cancer). This happens due to ignorance of the importance of having a medical check-up even though in good health. Doctors and researchers can prevent the development of tumor cells through treatment that begins with radiological examinations to identify the possibility of a person being affected by this disease. One of the most frequently used techniques is Mammography. This technique can detect the presence of tumor cells using advanced technology and several methods in displaying the patient's diagnostic results on low-dose X-rays in the form of mammogram images. The technology is inseparable from the methods used to identify the presence of tumor cells. In this study, we have proposed the CNN method based on the deep-CNN model to identify mammogram images in the detection of breast cancer cells with average evaluation results in terms of accuracy, precision, recall, specificity, and f-measure on mammogram image datasets of 99.52%, 99.72%, 99.31%, 99.72%, and 99.5%. These results showed that this method has a good performance in breast cancer detection. © 2023 IEEE.

关键词： Diseases

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Enhancing Brain Tumor Detection Using Convolutional Neural Networks in Medical 1

Enhancing Brain Tumor Detection Using Convolutional Neural N...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Dubey, Rishav Gund, Ms Pratima Tarambale, Manoj Shrivastava, Kapil Choudhari, Ashwini Suresh Dumne, Neha Baburao Manipal University Jaipur Department of Computer Science Jaipur India Pune India PVG's Coet and Gkp Iom Electrical Engineering Department Maharashtra Pune411009 India Department of Computer Engineering and Applications GLA University Mathura India Dhole Patil College of Engineering Department of Electronics & Telecommunication Engineering Pune India

ISBN: (纸本)9798350330915

It is commonly known that early and accurate brain tumour detection is critical, which emphasizes the need for better diagnostic techniques that improve patient outcomes. Although they are the mainstay of diagnosis, conventional imaging methods like CT and MRI scans are frequently limited by their requirement for extreme precision. In order to improve brain tumour detection in medical imaging, this study explores the effectiveness of convolutional neural networks, or CNNs, with a particular emphasis on the U-Net and DenseNet architectures. Through the use of a complex methodology that involves extensive validation procedures, rigorous model training, and data augmentation, the study shows a significant improvement in the accuracy of brain tumour detection. The results confirm that CNNs outperform conventional techniques in more accurately determining and categorizing brain tumour kinds and margins. This development clarifies the significant influence of deep learning in neuro-oncology, pointing to a crucial shift toward AI-enhanced diagnostic methods in clinical settings and establishing the framework for further study in more extensive and diverse datasets © 2023 IEEE.

关键词： Diagnosis

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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Multivariate Time-Series Deep Learning for Joint Prediction of Temperature and Relative Humidity in a Closed Space 8

Multivariate Time-Series Deep Learning for Joint Prediction ...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Gunawan, Fergianto E. Budiman, Arief S. Pardamean, Bens Juana, Endang Romeli, Sugiarto Cenggoro, Tjeng W. Purwandari, Kartika Hidayat, Alam A. Redi, Anak A.N.P. Asrol, Muhammad Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia PT Impack Pratama Industri Jakarta14350 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

An accurate predictive model of temperature and humidity plays a vital role in many industrial processes that utilize a closed space such as in agriculture and building management. With the exceptional performance of deep learning on time-series data, developing a predictive temperature and humidity model with deep learning is propitious. In this study, we demonstrated that deep learning models with multivariate time-series data produce remarkable performance for temperature and relative humidity prediction in a closed space. In detail, all deep learning models that we developed in this study achieve almost perfect performance with an R value over 0.99. © 2023 The Authors. Published by Elsevier B.V.

关键词： closed space deep learning humity prediction indoor temperature prediction

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Style Transfer of Musical Performance Expression Using Note Classification Based on the Implication-Realization Model 23

Style Transfer of Musical Performance Expression Using Note ...

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7th International Conference on computer science and Application engineering, CSAE 2023

作者： Mizutani, Tetsuya Sasaki, Shigefumi Department of Information Engineering University of Tsukuba Tsukuba Japan Master's Program in Computer Science University of Tsukuba Tsukuba Japan

ISBN: (纸本)9798400700590

In recent years, the style transfer is being studied in the field of AI. It extracts a style from one data set and applies to another data set. This technique is actively studied mainly in computer vision. It is also being studied in music and speech processing with many deep learning methods. However, no research focused on changes in performance expression of music. Performance expression of music refers to the player's artistic interpretation of a piece of music, the changes in performance, and the techniques used to achieve these changes. Changes in performance are represented by slow and fast beats, changes in strength and weakness. In this study, as a part of the study of analysis and creation of performance expression models of music, the image style transfer method is applied to musical expression. In addition, in order to analyze the relationship between musical structure and musical expression, the implication-realization model of music theory is used to extract the structure of music to improve the performance of style transfer. The analysis result shows that the I-R model captures the feature of musical expression well, and moreover, the style transfer method is shown to be useful for analyzing performance expression of music. It will lead to the study of music performance expression style extraction and will contribute to the analysis and creation of performance expression models of music. © 2023 ACM.

关键词： Music

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Intelligent Car Parking System Using WSN 6

Intelligent Car Parking System Using WSN

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6th Global Conference on Wireless and Optical Technologies, GCWOT 2023

作者： Ratti, Steve Azarsh Pirzada, Nasrullah Shah, Syed Mohammad Ali Naveed, Anjum Mehran University of Engineering & Technology Jamshro Department of Telecommunication Engineering Hyderabad Pakistan Mehran University of Engineering & Technology Hyderabad Pakistan Hazara University Mansehra Dept. of Computer Science & Technology Mansehra Pakistan

ISBN: (纸本)9798350333718

Numerous technologies are constantly being created due to the rapid rise in the standardization and automation of operational and management systems. It simplifies procedures and allows microelectronics technology to advance more often. It was managed by an Android app and controlled by sensor networks and parking systems. The wireless sensor network manages parking spot management, parking lot reservations, vehicle navigation, and other complex circumstances. This study suggests a wireless sensor network-based and android app-based intelligent parking system. Sensors installed beneath enable the cutting-edge parking system to function. The research suggests an intelligent parking system that uses wireless sensor networks and IR, magnetic, and light sensors. The ground-level sensors used by the advanced parking system function. The base station receives the node/slot information, and the base station delivers data to FTP. At the user's request, FTP transmits details about nodes and spaces to mobile devices. The idea came from using an IoT application to control the automobile parking system, which is crucial to our daily lives. For this purpose, a mobile application for Android called Smart Parker was developed. It provides information about a parking lot, such as available or reserved spaces. Sensors at the base station will send information about parking spaces to the FTP server. The user of the innovative payment system is required to pay to park the car. Cash is first collected using cash counters, but maintaining them is challenging. Later, different methods are employed to collect the payment. The payment is made by an Automated Vehicle Identification (AVI) tag using RFID technologies. Smart, debit, and credit cards are contact methods, whereas RFID technology and mobile devices are contactless alternatives. The smart parking system on FPGA (Finite State Machine Modeling) works on two main modules, i.e., the identification module and slot checking module. The

关键词： Internet of things

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Enabling Smart Home Energy Management through Gesture-Based Control and IoT Technology

Enabling Smart Home Energy Management through Gesture-Based ...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Rambe, Khairul Hawani Ou, Chia-Ho International Master Program in Information Technology and Applications Sweden National Pingtung University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

The Internet of Things (IoTs) have become ubiquitous in all aspects of public needs today. The application of IoT technology is crucial for promoting energy-saving behavior. This paper presents a control system used for smart home devices based on hand gesture control and Android applications. The hardware design uses the ESP32 microcontroller, which has an in-chip Wi-Fi module, making it very suitable for creating IoT application systems. The software was designed using the Arduino IDE, and the application display design was developed using Kodular Creator, which is used to control the lamps and switches. The experiment results show that the electrical equipment can be activated using the Android application with gesture sensors by waving down, up, left, and right. © 2023 IEEE.

关键词： Internet of things

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