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

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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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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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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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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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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An Object Driven Fuzzy Decision Model for Sugarcane Yield Prediction in Indonesia's Sugar Industry 7

An Object Driven Fuzzy Decision Model for Sugarcane Yield Pr...

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7th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2024

作者： Utama, Ditdit Nugeraha Asrol, Muhammad Suharjito Harito, Christian Napthaleni Syafrudin, Muhammad Bina Nusantara University BINUS Graduate Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Industrial Engineering Department Jakarta11480 Indonesia Sejong University Department of Artificial Intelligence and Data Science Seoul05006 Korea Republic of

ISBN: (纸本)9798331519643

A sugarcane yield of one plantation area depends on several independent variables. Practically it is challenging to predict accurately by using conventional methods. This study aims to develop a decision model based on a combination of fuzzy logic and object-oriented methods to predict sugarcane yield. The research is conducted in four main stages, employing object-oriented methods for model design and fuzzy logic for model construction. Object and activity diagrams are used for the object-oriented model design. The fuzzy membership functions employed are a combination of trapezoidal and triangular shapes. The resulting decision model can simulate 2,225 data from plantation areas in Indonesia. Based on the 10 examples of plantation area data in Indonesia, plantation number one obtained the largest sugarcane yield, which was 4.79%, with a similarity value of 0.90 (when compared to manual calculations as its ground truth). This similarity value is a higher value when compared to the average similarity value, which is 0.89. © 2024 IEEE.

关键词： Sugar industry

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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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Prediction of Internet Broadband Connectivity Classification in West Java Indonesia using Machine Learning

Prediction of Internet Broadband Connectivity Classification...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Kusuma Atmaja, Wahyu Haris Heryadi, Yaya Sonata, Ilvico Lukas Suparta, Wayan Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department West Jakarta Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Engineering Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

A location's Take-up Rate was significantly influenced by its Internet connectivity and availability. The purpose of this research is to answer concerns about internal Internet Service Provider issues that affect Internet connectivity in clusters of West Java, Indonesia. It also aims to evaluate the results of models that forecast how these internal factors will affect internet connectivity. According to the research findings, the FTTH Cluster has eight significant Internet predictor connectivity out of 18 elements. Activation delay and Administration Documentation of Activation with Connectivity were the two major parameters modeled out of the 18 components. Artificial Neural Network (ANN), Support Vector Machine (SVM), and Decision Tree (DT) models were used to predict Internet Broadband Connectivity Classification based on the two most important variables. According to the study, ANN is the best model, with 99% accuracy and 99.6% precision when compared to SVM and DT. Furthermore, it exceeds previous studies that used the ANN model and achieved 97.92% accuracy. © 2023 IEEE.

关键词： Prediction models

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A Hybrid Inference Engine to Predict Uncertain Sugar Content for Sugar Industry 9th

A Hybrid Inference Engine to Predict Uncertain Sugar Content...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Asrol, Muhammad Napthaleni Utama, Ditdit Nugeraha Suharjito Harito, Christian Syafrudin, Muhammad Industrial Engineering Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia Department of Artificial Intelligence and Data Science Sejong University Seoul05006 Korea Republic of

The sugar industry is facing challenges in increasing productivity to meet consumer demand. One opportunity for productivity improvement lies in ensuring sugar content. This study proposes a hybrid model to predict sugar content by considering uncertainty factors. A hybrid model combining fuzzy subtractive clustering, and a fuzzy inference system is proposed to predict sugar content. The clustering results using silhouette and fuzzy subtractive clustering successfully identified 6 cluster centres from 2225 datasets collected in a sugar industry in East Java Province. The hybrid inference engine model is designed with fuzzy rules derived from the clustered data. Two inference models are developed: triangular and Gaussian fuzzy numbers. The testing results indicate that the hybrid model with triangular fuzzy numbers shows the smallest error with an R2 value of 0.95. This model is possible to applied in the sugar industry for decision makers in improving productivity with taking attention into uncertain factors influencing sugar content. © 2024 The Authors.

关键词： Prediction models

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