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2022 IEEE Global Conference on Computing, Power and Communication Technologies, GlobConPT 2022

作者： Pilania, Urmila Diwakar, Chetna Arora, Khushi Chaudhary, Shruti Manav Rachna University Computer Science Department Haryana Faridabad India

ISBN: (数字)9781665493659

ISBN: (纸本)9781665493659

Cataract, a disease in which the lens of the eye slowly turns opaque resulting in blurry vision, is one of the most occurring diseases among older people. According to the sources, more than 50% of people aged 80 or more have had eye cataract. While cataracts only used to affect elderly people, nowadays it is prevalent to see them in ages below 50 due to modern lifestyles, lack of exercise, continuous usage of mobile phones and computers. Over 12 million people are blind in India and 62.6% of blindness is a result of cataract. If the detection is done before the final stages of cataract, this disease can be cured in less time. artificial Intelligence (AI) is a significant tool for the development of computer-aided technology. The retinal image is an important medical reference that helps in diagnosing cataract. This paper proposes a hybrid method using the Fuzzy Technique, Retinal image Quality Assessment (RIQA), and Convolutional neural networks (CNN). The fuzzy technique is used for image quality improvement, RIQA is used for image quality assessment and CNN is used for processing of the retina to automatic cataract detection. © 2022 IEEE.

关键词： Convolutional neural networks

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Improved VGG-16 neural Network for Parameter Reduction 8th

Improved VGG-16 Neural Network for Parameter Reduction

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8th International Conference on artificial Intelligence and Security , ICAIS 2022

作者： Jiang, Zheng Zou, Kaiwen Yao, Jiangyuan Li, Deshun Cao, Xingcan Hainan University Haikou570228 China University of British Columbia VancouverBCV6T1Z1 Canada

ISBN: (纸本)9783031067662

With the development of Deep Learning, image recognition technology has been applied in many aspects. And convolutional neural networks have played a key role in realizing image recognition under the increasing computing power and massive data. However, if developers want to implement the training of convolutional neural networks and achieve the subsequent applications in scenarios such as personal computers, IoT devices, and embedded platforms with low Graphics processing Units(GPUs) memory, a large number of parameters during training of convolutional neural networks is a great challenge. Therefore, this paper uses depthwise separable convolution to optimize the classic convolutional neural network model VGG-16 to solve this problem. And the VGG-16-JS model is proposed using the Inception structure dimensionality reduction and depthwise separable convolution on the VGG-16 convolutional neural network model. Finally, this paper compares the classification success rates of VGG-16 and VGG-16-JS for the application scenario of the COVID-19 mask-wearing. A series of reliable experimental data show that the improved VGG-16-JS model significantly reduces the number of parameters required for model training without a significant drop in the success rate. It solves the GPU memory requirements for training neural networks to a certain extent. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Human Detection with A Feedforward neural Network for Small Microcontrollers 7

Human Detection with A Feedforward Neural Network for Small ...

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7th International Conference on Frontiers of Signal processing, ICFSP 2022

作者： Wulfert, Lars Wiede, Christian Verbunt, Martin H. Gembaczka, Pierre Grabmaier, Anton Fraunhofer Ims Embedded Ai Duisburg Germany University of Duisburg-Essen Faculty of Electrical Engineering Duisburg Germany

ISBN: (数字)9781665481588

ISBN: (纸本)9781665481588

Motion detection is widely used in smart home applications. Nevertheless, it is crucial that a non-human movement does not trigger a motion sensor for various applications. For this purpose, camera-based sensors and machine learning are used. However, modern machine learning, such as deep neural networks (DNN), requires powerful hardware, which is expensive, has high power consumption, and requires a significant amount of storage space. Therefore, DNN object detection methods cannot be used directly on resource-constrained devices as the models require too much storage space. We present an object detection method for a resource-limited system where a camera-based human detection is computed directly on a small microcontroller to overcome these limitations. The key innovation enables object detection on a microcontroller with a small storage space and low computational power. With a unique image pre-processing and a feedforward artificial neural network (ANN), we reached a parameter reduction of the ANN of over 99 % compared to DNN image classification and object detection methods, allowing us to use the algorithm on an ESP32 with a detection rate of 83 ms. Furthermore, we also propose an approach that automatically generates a dataset from videos for the training of the ANN. © 2022 IEEE.

关键词： Feature extraction

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TODOS: Thermal sensOr Data-driven Occupancy Estimation System for Smart Buildings 23

TODOS: Thermal sensOr Data-driven Occupancy Estimation Syste...

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10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys)

作者： Rajabi, Hamid Ding, Xianzhong Du, Wan Cerpa, Alberto Univ Calif Merced Merced CA 95343 USA

ISBN: (纸本)9798400702303

Occupancy sensing and estimation in large commercial buildings has become a significant problem to be solved, with applications ranging from occupancy-based HVAC control to space planning, and security, etc. Thermal sensing is a promising technology to solve this problem, being easy to deploy in practice and allowing an actual occupancy count in a particular room without violating the data and privacy concerns. While initial strides have been made to solve this problem with thermal arrays, there are many problems that remain unsolved, including accuracy performance, overlapping of sensing areas that lead to under/over-counting, and data training requirements for different zones. In this paper, we introduce TODOS1, a novel system for estimating occupancy in intelligent buildings. TODOS uses a low-cost, low-power thermal sensor array along with a passive infrared sensor. We introduce a novel data processing pipeline that allows us to automatically extract features from the thermal images using an artificial neural network. Through an extensive experimental evaluation(2), we show that TODOS provides occupancy detection accuracy of 98% to 100% under different scenarios. In addition, it solves the issue of occupancy over/under-counting by overlapping sensing areas when using multiple thermal sensors in large rooms. This is done by treating the entire area as a single input thermal image instead of partitioning the area into multiple thermal images individually processed. Furthermore, TODOS introduces a data augmentation technique that allows the generation of training data for rooms of different sizes and shapes, without requiring specific training data from each room. Using these data, TODOS can train specifically designed neural networks optimized for any room size and shape, and achieve almost the same level of occupancy detection accuracy in rooms where experimental labeled training data is available, making it a viable solution that generalizes to the different room

关键词： smart buildings occupancy estimation HVAC systems thermal occupancy sensor neural networks data augmentation

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Research on Human Resource Management and Prediction Model Based on neural Network Optimized by Genetic Algorithm

Research on Human Resource Management and Prediction Model B...

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image processing and Computer applications (ICIPCA), IEEE International Conference on

作者： Zexuan Qi Sihan Yang Xinjian He Desheng Shi Xinjiang University of Science & Technology Korla Xinjiang China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Engineering Guangzhou Huali College Guangzhou China Modern Agricultural College Honghe Vocational and Technical College Mengzi Yunnan China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This paper investigates the application of artificial neural networks optimized based on genetic algorithms in human resource management in hospital enterprises and constructs a human resource management and predictive analysis model. The model aims to support enterprises in achieving the optimal allocation of human resources by mining their internal human resource data and revealing the intrinsic correlation between personnel structure, business type and future development strategy. The study adopts multiple regression analysis method and combines the artificial neural network optimized by genetic algorithm to construct a human resource demand prediction model. Taking the operational data of Hospital X as an example, the model predicts the number of human resources in the hospital and analyzes in depth the demand for staff types in each department. The results show that the model has high accuracy and reliability in predicting the variables of hospital revenue, wage level, R&D investment and number of personnel, with a prediction error range between 0.09% and 1.27%. The research in this paper provides strong data support for enterprises, which helps them to realize the rational allocation of human resources and strategic transformation, and then maintain their competitive position in the market.

关键词： Analytical models Hospitals artificial neural networks Predictive models Data models Regression analysis Resource management Personnel Predictive analytics Genetic algorithms

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MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning

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artificial Intelligence in Geosciences 2022年第1期3卷 101-114页

作者： Tariq Alkhalifah Hanchen Wang Oleg Ovcharenko Physical Sciences and Engineering King Abdullah University of Science and TechnologyMail box#1280Thuwal 23955-6900Saudi Arabia

Among the biggest challenges we face in utilizing neural networks trained on waveform(i.e.,seismic,electromagnetic,or ultrasound)data is its application to real *** requirement for accurate labels often forces us to train our networks using synthetic data,where labels are readily ***,synthetic data often fail to capture the reality of the field/real experiment,and we end up with poor performance of the trained neural networks(NNs)at the inference *** is because synthetic data lack many of the realistic features embedded in real data,including an accurate waveform source signature,realistic noise,and accurate *** other words,the real data set is far from being a sample from the distribution of the synthetic training ***,we describe a novel approach to enhance our supervised neural network(NN)training on synthetic data with real data features(domain adaptation).Specifically,for tasks in which the absolute values of the vertical axis(time or depth)of the input section are not crucial to the prediction,like classification,or can be corrected after the prediction,like velocity model building using a well,we suggest a series of linear operations on the input to the network data so that the training and application data have similar *** is accomplished by applying two operations on the input data to the NN,whether the input is from the synthetic or real data subset domain:(1)The crosscorrelation of the input data section(i.e.,shot gather,seismic image,etc.)with a fixed-location reference trace from the input data section.(2)The convolution of the resulting data with the mean(or a random sample)of the autocorrelated sections from the other subset *** the training stage,the input data are from the synthetic subset domain and the auto-corrected(we crosscorrelate each trace with itself)sections are from the real subset domain,and the random selection of sections from the real data is implemented at every epoch of

关键词： neural networks Induced seismicity image processing Computational seismology Waveform inversion

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Exploration and Evaluation of Machine Learning Methods for Detecting Oil Spills in Open Seas

Exploration and Evaluation of Machine Learning Methods for D...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Fadi N. Sibai Ahmad Sibai Ali El-Moursy GUST Engineering & Applied Innovation Research Center Gulf Univ. for Science and Tech. West Mishref Kuwait Computer Science & Engg. Dept. Univ. of South Florida Tampa Florida U.S.A. Comp. Engg. Dept. Univ. of Sharjah Sharjah U.A.E.

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Detecting oil spills in open seas is important for the early containment of the pollution diffusion and avoidance of further ecological catastrophes. Herein, we explore and assess the application of several machine learning (ML) methods and image processing techniques for the detection of oil spills in images of open seas captured at the elevation of planes, helicopters, or drones. The image dataset used contains 24 clean sea images and 24 dirty sea images with oil slicks. The machine learning methods experimented with include neural networks, SVM, random forest, decision tree, naïve Bayes, AdaBoost, and logistic regression. We also evaluated these ML methods with and without the application of Sobel Feldman edge detection and Vintage filtering image processing techniques. Results indicate the superiority of the in and neural networks methods over the other explored ML methods, achieving perfect and near-perfect classification, respectively.

关键词： Support vector machines Logistic regression Oils image edge detection image processing Helicopters artificial neural networks Satellite images Random forests Drones

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Integrating image Steganography Techniques Into an Existing Efficient image Recognition artificial neural Network for Optimal Performance

Integrating Image Steganography Techniques Into an Existing ...

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IEEE Power Engineering Society Conference and Exposition in Africa, PowerAfrica

作者： Matimu Caswell Nkuna Ahmed Ali Ebenezer Esenogho Department of Electrical & Electronic Engineering Science University of Johannesburg Johannesburg South Africa Department of Electrical Engineering University of Botswana Gaborone Botswana

ISBN: (数字)9798350389388

ISBN: (纸本)9798350389395

image steganography is a data-hiding process whereby a digital image media is used as a cover to encrypt and embed data within the pixels of the image. This method can be applied in various research fields, including image processing, power line systems, mobile applications, etc. This paper investigates the application of an image steganography technique that differs from traditional least significant bit substitution methods. The discrete cosine transform least significant bit-2 method is proposed in this paper. Rather than placing the hidden data bits in a deterministic manner, the proposed method inserts the data bits in random pixel locations within the cover image to alleviate steganalysis and unauthorized access to the hidden data. An image recognition artificial neural network developed using open source TensorFlow is applied and used to test the effectiveness of the prosed encryption method. The proposed encoding scheme shows more robustness and allows for higher neural network prediction confidence. This method can be applied in power line systems to determine the physical conditions of power lines, such as when they are damaged/broken. The image recognition artificial neural network can also forecast power line maintenance cycles for proactive monitoring of power systems.

关键词： Resistance Steganography image recognition image coding artificial neural networks Robustness Encoding Maintenance Power systems Discrete cosine transforms

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Comparison of Vision Transformer with Convolutional neural networks for Brain Cancer Classification 17

Comparison of Vision Transformer with Convolutional Neural N...

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17th IEEE International Conference on Computer Research and Development, ICCRD 2025

作者： Manali, Dogu Demirel, Hasan Eastern Mediterranean University Electrical and Electronic Engineering Famagusta Cyprus

ISBN: (纸本)9798331531881

Brain cancer is one of the most deadly illnesses. It causes abnormal cells to grow in the brain. Planning for treatment and the prognosis of patients with brain tumors depend greatly on early diagnosis. Brain tumors can have different characteristics, treatments, and forms. Consequently, the process of manually detecting brain tumors is difficult, labor-intensive, and error-prone. Doctors use magnetic resonance imaging to detect those abnormal cells in the brain. With the growth of artificial intelligence, it is possible to diagnose the brain tumor from MIR images. For instance, convolutional neural networks and transformers could be used. The self-Attention mechanism is implemented by transformers, which are models that give each input data component a distinct weight. Transformers have limited applications in image classification tasks because they were originally designed for use in natural language processing applications. Thus far, the majority of image classification research has employed convolutional neural networks. In this paper, six different pretrained convolutional neural networks and a vision transformer are used to classify four distinct brain tumor classes. The models include ResNet50, AlexNet, VGG16, InceptionV3, MobileNetV2, FractalNet, and the Vision Transformer. The goal of this study is to compare the performance of these pretrained convolutional neural network models with that of the vision transformer, demonstrating that transformers can also be effectively applied to image classification tasks. The performance of a vision transformer model shows 84.39% accuracy in the classification problem, which is better than the other six architectures. © 2025 IEEE.

关键词： Convolutional neural networks

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Integrating CNN and SVM for Enhanced Classification of Maize and Sugarcane Leaf Diseases

Integrating CNN and SVM for Enhanced Classification of Maize...

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2024 International Conference on artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Gupta, Meghna Jain, Sarika Kumar, Manoj Amity Institute of Information Technology Amity University Noida Noida India University of Wollongong Faculty of Engineering & Information Sciences Dubai United Arab Emirates

ISBN: (纸本)9798350379716

Analyzing a crop accurately and managing its diseases precisely are required for increased agricultural output and assured food security. In image processing, CNN has emerged as a powerful and dominant technique. This research provides an approach to detect diseases in Kharif crops (sugarcane and maize) early on using hybrid deep learning architectures, such as Svm and Cnn. This research aims to blend valuable insights from hybrid crop identification and disease management techniques to foster sustainable farming practices. Maize is considered the third most important crop in India, contributing around 10% of total food grain production in the country whereas sugarcane contributes around 1.1% to India's GDP. Using visual data in the form of images, we have employed a hybrid strategy to categorize diseases identified in sugarcane and maize. Using a dataset of 14,000 photos, we have created 7 different classes, 4 of which are related to maize diseases and 3 to sugarcane diseases. Using CNN, high-level features have been extracted and classified using SVM. Training accuracy of the proposed model with the help of CNN used for feature extraction and SVM used for classification was obtained at 99.19%, validation accuracy was obtained at 82.64%, and test accuracy was achieved at 82.64%. High performance and outstanding discriminative capacity were shown through the confusion matrix and ROC analysis with an average AUC value of 0.92 across all classes. The hybrid technique shows prominent advantages over standalone CNN models with the help of possible applications in agricultural diagnostics and offers a feasible alternative for precise and effective finding of maize and sugarcane diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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