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Dynamic Multi-Layer Perceptron for Fetal Health Classification Using Cardiotocography Data

computers, Materials & Continua 2024年第8期80卷 2301-2330页

作者： Uddagiri Sirisha Parvathaneni Naga Srinivasu Panguluri Padmavathi Seongki Kim Aruna Pavate Jana Shafi Muhammad Fazal Ijaz Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Amrita School of Computing Amrita Vishwa VidyapeethamAmaravatiAndhra Pradesh522503India Department of Tele informatics Engineering Federal University of CearáFortaleza60455-970Brazil Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaramGunturAndhra Pradesh522302India Department of Computer Engineering Chosun UniversityGwangju61452Republic of Korea School of CSIT Symbiosis Skills and Professional UniversityPune412101India Department of Computer Engineering and Information College of Engineering inWadi AlddawasirPrince Sattam bin Abdulaziz UniversityWadi Alddawasir11991Saudi Arabia School of IT and Engineering Melbourne Institute of TechnologyMelbourne3000Australia

Fetal health care is vital in ensuring the health of pregnant women and the *** check-ups need to be taken by the mother to determine the status of the fetus’growth and identify any potential *** know the status of the fetus,doctors monitor blood reports,Ultrasounds,cardiotocography(CTG)data,***,in this research,we have considered CTG data,which provides information on heart rate and uterine contractions during *** researchers have proposed various methods for classifying the status of fetus *** processing of CTG data is time-consuming and ***,automated tools should be used to classify fetal *** study proposes a novel neural network-based architecture,the Dynamic Multi-Layer Perceptron model,evaluated from a single layer to several layers to classify fetal *** strategies were applied,including pre-processing data using techniques like Balancing,Scaling,Normalization hyperparameter tuning,batch normalization,early stopping,etc.,to enhance the model’s performance.A comparative analysis of the proposed method is done against the traditional machine learning models to showcase its accuracy(97%).An ablation study without any pre-processing techniques is also *** study easily provides valuable interpretations for healthcare professionals in the decision-making process.

关键词： Fetal health cardiotocography data deep learning dynamic multi-layer perceptron feature engineering

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Machine Learning for Strategic Sales Forecasting in Retail Chains

Machine Learning for Strategic Sales Forecasting in Retail C...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Rajaram, Tharanimathi Udhayamoorthi, M. Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore641042 India

ISBN: (纸本)9798331510299

Sales of an individual item record maintained by supermarket chains/big retailers like Big Mart help in estimating consumer demand and thereby enable them to do effective inventory management. Through the analysis of data stored in the data warehouses, they access the anomalies and trends informing decision-making processes. This will be invaluable information to the retailers, in this case Big Mart, in predicting the future sales volume using machine learning techniques. A predictive model was developed utilizing XGBoost, Linear Regression, Logistic Regression, and SVM techniques to forecast sales for businesses like Big Mart. It was found that this model outperforms existing ones, providing more accurate sales predictions and enabling better inventory management decisions. © 2024 IEEE.

关键词： Chains

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Robust autonomous driving control using deep hybrid-learning network under rainy/snown conditions

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Multimedia Tools and Applications 2024年第41期83卷 89281-89295页

作者： Lee, Chao-Yang Khanum, Abida Sung, Tien-Wen Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Yunlin Taiwan Department of Electrical Engineering National Cheng Kung University No.1 University Road Tainan City701 Taiwan College of Computer Science and Mathematics Fujian University of Technology No3 Xueyuan Road Fuzhou350011 China

The study introduces a groundbreaking two-stage deep hybrid learning architecture, Robust Autonomous Driving Control (RADC), designed to address the formidable challenge of ensuring safe and efficient autonomous driving in adverse weather conditions, including heavy rain and snow, in complex scenarios. In the first stage, our proposal utilizes an encoder as a variational autoencoder (VAE) model. This encoder leverages the VAE to extract feature information from the perceptual data surrounding the environment. Moving on to the second stage, we build a decoder as an Inception-Bidirectional Long Short-Term Memory (IBL) model. This decoder combines the VAE latent features obtained in stage one with additional control vehicle information tasks, including steering, speed, etc. Our approach involves predicting driving behavior along a predetermined route, allowing autonomous vehicles to stay centered on the road, simulating diverse driving scenarios, and achieving significant reductions in route-following time. This framework utilizes deep hybrid learning methods and harnesses Nvidia GPU capabilities to evaluate the effectiveness of InceptionNet, ResNet-50, MobileNet, DenseNet, and VGG16 convolutional neural networks. It enhances vehicle route following with improved metrics such as reduced inference time, heightened accuracy, and minimized lane changes in training. The RADC demonstrates exceptional performance, notably achieving a mean square error of 0.0464, 0.0346, and a 6-millisecond inference time in challenging weather like hard rain and snow. The proposed model is validated through comprehensive experiments in the Airsim environment, showcasing notable interpretability, generalization, and robustness capabilities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Decision making

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Cluster Head-Based Wireless Sensor Network Sustainability Algorithm 2

Cluster Head-Based Wireless Sensor Network Sustainability Al...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Sree Pokkuluri, Kiran Aswini, P. Chandrashekar, Rakesh Sirisha, N. Kumar, Manish Adnan, K. Shri Vishnu Engineering College for Women Department of Computer Science and Engineering Bhimavaram India Hyderabad India New Horizon College of Engineering Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (纸本)9798350364699

The growth of a reduced-capacity assembling contract to communicate data from channel heads to BSs (Base Stations) is a main concern in WSNs, and this item outlines individual specific resolutions. In WSN, a multi-level whole of order is beneficial for the data group and destroying. Increasing the network's old age is one of the benefits of hierarchic assembling procedures. The group of the cluster head by way of the use of the centre pattern is the big focus of these algorithms, accompanying the main objective search to embellish the original centroid selection technique. To guarantee the network's staying power, the submitted process takes many determinants into report when selecting a Cluster Head (CH), containing leftover strength, range middle from two points growth, CHs, and the base station. The overall depiction of the imitation is judged distinguished from the depiction of usual algorithms, and effective strength habit is obtained. © 2024 IEEE.

关键词： Hierarchical clustering

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Skin Disease Detection Using Convolutional Neural Network 10

Skin Disease Detection Using Convolutional Neural Network

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Priyanka, G. Dhanabal, D. Divya, D. Hemanth, M. Karthika, V. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (纸本)9798350384369

Skin conditions are common everywhere, and early and precise diagnosis is essential for successful treatment. This work gives a thorough investigation of the use of Python programming and machine learning techniques for the prediction of skin disorders. In the suggested method, several machine learning algorithms are trained and assessed using a wide dataset of dermatological photos. The study starts with preprocessing methods to get the data ready for modeling, such as picture improvement and feature extraction. Then, using well-known Python libraries like scikit-learn and TensorFlow, a variety of classification methods are constructed, including Support Vector Machines, Random Forests, and Convolutional Neural Networks. To maximize the models' performance, cross-validation techniques are used for training and fine-tuning. The findings demonstrate the usefulness of the created models in properly predicting skin disease classes. Performance parameters such as accuracy, precision, recall, and F1-score are presented and compared between algorithms. The suggested Python-based system has considerable potential for supporting dermatologists and healthcare practitioners in the early detection and management of skin problems. © 2024 IEEE.

关键词： Convolutional neural networks

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Forecasting Directional Movement of Stock Prices using Deep Learning

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Annals of Data science 2023年第5期10卷 1361-1378页

作者： Chandola, Deeksha Mehta, Akshit Singh, Shikha Tikkiwal, Vinay Anand Agrawal, Himanshu Department of Electrical Engineering and Computer Science York University Toronto Canada Hebert Werthim College of Engineering University of Florida Gainesville Gainesville United States Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Stock market’s volatile and complex nature makes it difficult to predict the market situation. Deep Learning is capable of simulating and analyzing complex patterns in unstructured data. Deep learning models have applications in image recognition, speech recognition, natural language processing (NLP), and many more. Its application in stock market prediction is gaining attention because of its capacity to handle large datasets and data mapping with accurate prediction. However, most methods ignore the impact of mass media on the company’s stock and investors’ behaviours. This work proposes a hybrid deep learning model combining Word2Vec and long short-term memory (LSTM) algorithms. The main objective is to design an intelligent tool to forecast the directional movement of stock market prices based on financial time series and news headlines as inputs. The binary predicted output obtained using the proposed model would aid investors in making better decisions. The effectiveness of the proposed model is assessed in terms of accuracy of the prediction of directional movement of stock prices of five companies from different sectors of operation. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Large dataset

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IOT and Machine Learning Based Real-Time Protection Against Cyber-Physical Threat Mitigation 4

IOT and Machine Learning Based Real-Time Protection Against ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Saini, Harnit Mohammad, Faz Singh, Manoj Kumar Sharma, Kapil Kumar Shukla, Meenu Singh, Ramendra Ajay Kumar Garg Engineering College Department of Computer Science Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Ims Engineering College Department of Computer Science & Engineering Ghaziabad India Ims Engineering College Department of Mca Ghaziabad India Maharishi University of Information Technology School of Engineering and Technology Noida India Ghaziabad India

ISBN: (纸本)9798350360165

An investigation is carried out into the dynamic relationship between machine learning algorithms used in IoT security, with detailed scrutiny of performance indicators to give a fuller view of what happens. The study pays particular attention to metrics like accuracy, precision, recall, and F1 score when ten varied samples are being examined. As a result, all the algorithms exhibit excellent performance with 95.5 %-98.2 % accuracy values, inspiring high reliability under diverse scenarios. Precision and recall metrics also depict the models' capabilities. Balancing precision and recall can be observed in F1 scores that are between 95.2% and 98.0%, and these numbers show that the approach based on this understanding of trade-offs is characterized by its operationalization. At the other end, results obtained from performance metrics go beyond numerical benchmarks to give a holistic appreciation of the algorithms' strength and areas for possible enhancement. The paper also does not provide only a theoretical approach to machine learning related to security in the IoT environment but also provides information about security considerations for the successful deployment of powerful, adaptive models into real-life scenarios. In this case, findings of the research help in understanding how to make sure that smart and effective measures are implemented during the process of developing security solutions considering all related issues in connection with the IoT technology landscape. © 2024 IEEE.

关键词： Adversarial machine learning

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Detection of Ripe and Raw Tomatoes using Internet of Things 7

Detection of Ripe and Raw Tomatoes using Internet of Things

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Vanitha, C.N. Malathy, S. Krishna, S.A. Manikantan, M. Kumar, P. Dharun Gowshik, M. Cibi Department of Computer Science and Engineering Kongu Engineering College Erode Perundurai India Department of Mechatronics Engineering Kongu Engineering College Erode Perundurai India Department of Computer Technology Kongu Engineering College Erode Perundurai India

ISBN: (纸本)9781665464086

Agriculture is the backbone of humankind. Labour plays a major role in agriculture, particularly in tomato cultivation. Tomatoes play an important role in human diet. On the other hand, tomato harvesting is becoming increasingly difficult due to labour shortage. As a result, the price of tomatoes has continued to increase. This makes it difficult for farmers and exporters to sell and buy tomatoes. The primary goal is to assist farmers. Farmers typically look for ripe tomatoes to sell, and they identify ripe tomatoes based on their colour. The sensors included in the proposed model automatically separate the harvested tomatoes into ripe [red] and raw [green] tomatoes. Using this information, the colour sensing technique is used to make farmers' jobs easier when harvesting tomatoes. This research also intends to assist farmers in identifying the ripe and raw tomatoes and separate them accordingly. This classification process can be applied either before harvesting in the field or after collecting them from the storage. © 2023 IEEE.

关键词： Internet of things

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Challenges in Securing UAV IoT Framework: Future Research Perspective

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2607-2629页

作者： Aljumah, Abdullah Ahanger, Tariq Ahamed Ullah, Imdad Prince Sattam Bin Abdulaziz University College of Computer Engineering and Sciences Saudi Arabia CoBA Prince Sattam Bin Abdulaziz University Department of Management Information Systems Saudi Arabia The University of Sydney School of Computer Science Faculty of Engineering SydneyNSW2006 Australia

Unmanned Aerial Vehicles (UAVs) offer the immense capability for allowing novel applications in a variety of domains including security, military, surveillance, medicine, and traffic monitoring. The prevalence of UAV systems is due to the collaboration and accomplishment of tasks efficiently and effectively. UAVs embedded with camcorders, GPS receivers, and wireless sensors propose enormous promise in realizing the Internet of Things (IoT) service delivery in vast domains. It results in establishing an airborne field of the IoT when empowered with communication protocols of LTE, 4G, and 5G/6G networks. However, numerous difficulties must be addressed before UAVs may be used effectively namely privacy, security, and administration. Conspicuously, in the current article, novel UAV-specific domains enabled by IoT and 5G/6G technology are explored. Moreover, the presented technique assesses sensor requirements and provides an overview of fleet management systems that address aerial networking, privacy, and security concerns. Furthermore, a framework based on the IoT-5G/6G aspect is proposed which can be deployed over UAVs. Finally, in a heterogeneous computational platform, the proposed framework provides a complete IoT architecture that enables secure UAVs. © 2004-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Wave Deep Neural Network for Multi-Class MRI Brain Image Classification 2

Wave Deep Neural Network for Multi-Class MRI Brain Image Cla...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Ramasamy, Viswanathan Vinodhini, V. Saravanan, T.R. Kumar, S. Dinesh Lavanya, S. Kl University Department of Computer Science and Engineering Andhra Pradesh Vijayawada India Sona College of Technology Department of Computer Science and Engineering Tamil Nadu Salem India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Muthayammal Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India

ISBN: (纸本)9798350347579

The human brain is considered to be the most important organ in the body. Since the causes of brain cancer is still unknown, early detection is required for proper treatment. Magnetic Resonance Imaging (MRI) is an imaging technology used to depict inside structure of human body in details. This research study makes a contribution to the development of an image-based classification system as well as to the detection of brain cancer. Texture-based energy descriptors are retrieved using Discrete Wavelet Transform (DWT)-based sparse representation systems. A Convolutional Neural Network (CNN) in wavelet domain is used for further processing in order to improve the classification between normal and abnormal classes of MRI brain images. The performance of the proposed system is assessed by utilizing the standard collection of brain tumor images available in the REMBRANDT database. Results prove that the proposed system provides 98% accurate results for brain cancer classification. © 2023 IEEE.

关键词： Magnetic resonance imaging

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