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Applied Intelligence and Computing (AIC), IEEE World conference on

作者： J. Amutha S. Priyadarsini S. Prasanth M. R. Senkumar M. Ramnath T. Jasperline Department of Computer Science and Engineering P.S.R Engineering College Sivakasi Tamil Nadu India Department of Computer Science and Engineering Unnamalai Institute of Technology Kovilpatti Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Computer Science and Engineering Dr.G.U.POPE College of Engineering Sawyerpuram Tuticorin Tamil Nadu India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Oral Cancer of the mouth kills millions of people, Oral cancer is better treated and more often survived when caught early. Convolutional neural networks (CNNs) have recently shown remarkable promise for cancer diagnosis and other medical image processing applications. In this study, we train deep convolutional neural networks (CNNs) to detect oral cancer early. The suggested approach makes use of a large database of pictures of the mouth and its cavities. Develop and refine a convolutional neural network (CNN) model using the use of radiographs, histology slides, and intraoral images. Oral carcinogenic tumours, both precancerous and malignant, may be identified and classified automatically using this approach. The process begins with picture pre-processing, continues with dataset supplementation for better model generalizability, and culminates in the use of complex neural networks for feature extraction and learning. Transfer learning methods modify previously taught models for this medical imaging task. The system’s performance, sensitivity, and accuracy are evaluated using a set of images that have pre-existing diagnoses. Deep learning CNNs were able to identify oral cancer with an efficiency and accuracy of 93.62%, according to the research. The trained model is a valuable tool for early cancer screening because of its high sensitivity and specificity. By automating the process, oral cancer may be detected and treated more quickly, which improves patient outcomes and decreases the disease burden.

关键词： Accuracy Sensitivity Histopathology neural networks Transfer learning Mouth Sensitivity and specificity Convolutional neural networks Lesions Cancer

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Two Partially Reconfigurable Architectures For Efficient Implementation of Convolutional neural networks (CNNs

Two Partially Reconfigurable Architectures For Efficient Imp...

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International Engineering conference (IEC)

作者： Hakem Beitollahi

Today, convolutional neural networks (CNN) are widely used in many applications of artificial intelligence (AI), including image processing, video processing, natural language processing, and forecasting time series. CNNs require heavy computations to provide significant accuracy for many AI tasks. Normally, the training phase of CNNs is done through GPUs. However, the inference phase, which runs on the edge devices, is executed through a dedicated hardware accelerator. Therefore, the efficient implementations of CNNs to improve performance using limited resources without accuracy reduction is a challenge for AI systems for edge devices. One of the architectures for the efficient execution of CNNs is the array-based accelerator that consists of an array of similar processing Elements (PEs). The array accelerators are popular as high-performance architecture using the features of parallel computing and data reuse. These accelerators are optimized for a set of CNN layers, not for individual layers. Using the same accelerator dimension size to compute all CNN layers with varying shapes and sizes leads to the resource underutilization problem. To handle this challenge, two research teams under my supervision propose two solutions namely CNNX and RASHT in last few years. In my talk, I will discuss both techniques and explain how we can have a flexible and scalable architecture for array-based accelerator that increases resource utilization. The RASHT architecture resizes PEs depending on the size and shapes of the layers. The CNNX implements a small and efficient accelerator and then using retiling executes the layers with different sizes and shapes. Both architectures have been evaluated through different CNN structures such as GoogLeNet, MobileNet and AlexNet. Experimental results show the effectiveness of both proposed hardware accelerators.

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A CNN-Based Approach for Room Number Detection Using Drone in Indoor Environment

A CNN-Based Approach for Room Number Detection Using Drone i...

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Power, Electrical, and Electronics and Industrial applications (PEEIACON), IEEE International conference on

作者： Shorup Chanda Ranat Das Prangon Kazi Naimul Hoque Department of Mechanical Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Chemical Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Naval Architecture and Marine Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (数字)9798331517984

ISBN: (纸本)9798331517991

The revolutionary development of drone technology has significantly transformed various sectors such as security, construction, and transportation. By using computer vision and artificial intelligence, drones can now autonomously execute tasks that previously relied on human intervention, resembling intelligent robots. In this evolving landscape, drones have emerged as promising solutions, particularly for indoor applications, due to their safety and efficiency advantages. Despite these advancements, challenges persist, especially in ineffective navigation and airspace management within indoor environments. To address this gap, this current study emphasizes on utilizing a convolutional neural network (CNN) for room number detection within indoor drone systems, which is essential for ensuring precise product delivery. The drone is equipped with a Pixhawk flight controller and a Raspberry Pi for image capture and processing using a CNN model implemented in Keras based on the collected data. Our CNN model has achieved a recognition accuracy of 92.4%, outperforming conventional models in recall and F1 Score, highlighting the effectiveness of CNN-based solutions for advancing indoor drone operations and improving delivery services. The integration of image processing into real-time recognition systems mounted on drones holds immense potential for enhancing delivery efficiency and accuracy. This research demonstrates the transformative capabilities of CNN-based solutions in advancing indoor drone operations, promising more reliable and efficient services in the future.

关键词： Accuracy Transportation Streaming media Real-time systems Data models Indoor environment Convolutional neural networks Reliability Security Drones

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Comparative Analysis of Machine Learning and Deep Learning Algorithms for Plant Disease Classification and Detection

Comparative Analysis of Machine Learning and Deep Learning A...

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Advanced Computing & Communication Technologies (ICACCTech), International conference on

作者： Harminder Kaur Neeraj Raheja Amit Kumar Bindal Department of Computer Science and Engineering Maharishi Markandeshwar Engineering College Maharishi Markandeshwar (Deemed to be University) Mullana Ambala Haryana India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

Agriculture, a crucial foundation for human existence, exists at the intersection of conventional methods and modern innovations. Any country's primary issue in agriculture science is effective plant disease control, driven by the growing need for food due to population growth. Moreover, developments in modern technology have greatly improved the precision and effectiveness of plant detection. A critical challenge in the agricultural sector is leaf diseases, significantly impacting crop production and economic profit. Integrating digitalization and technology in agriculture is imperative for augmenting productivity and ensuring food security. This research emphasizes the principal concerns and obstacles associated with classifying leaf diseases. By experimenting with different approaches, researchers have already achieved great strides in the identification and categorization of illnesses. But due to assessments, new information, and discussions, changes are required. The resilience of deep learning (DL) and machine learning (ML) techniques, such as feed-forward neural networks (FFNN), k-means clustering (KMC), fuzzy logic (FL), artificial neural networks (ANN), and genetic algorithms (GA),so forth, has been demonstrated by earlier research. Due to their inherent ability to automatically obtain relevant visual information and understand spatial hierarchies, CNNs are usually the preferred option for image identification and classification among the ML and DL methods addressed in this study. The selection between machine learning and deep learning is dependent upon numerous issues, the data that is available, and the amount of processing power that is available. Because of this, DL—primarily using CNNs are advised for many complicated image detection and classification applications. They also show outstanding results for classification and detection on their datasets but not on others. Lastly, by using a variety of methods for processing images in the field of artific

关键词： Deep learning Fuzzy logic Support vector machines Plant diseases Visualization Technological innovation Feature extraction Agriculture Data mining Convolutional neural networks

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4.5 BioAIP: A Reconfigurable Biomedical AI Processor with Adaptive Learning for Versatile Intelligent Health Monitoring

4.5 BioAIP: A Reconfigurable Biomedical AI Processor with Ad...

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IEEE International conference on Solid-State Circuits (ISSCC)

作者： Jiahao Liu Zhen Zhu Yong Zhou Ning Wang Guanghai Dai Qingsong Liu Jianbiao Xiao Yuxiang Xie Zirui Zhong Hongduo Liu Liang Chang Jun Zhou University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728195490

ISBN: (纸本)9781728195506

Intelligent health monitoring devices automatically detect abnormalities in users' biomedical signals (e.g. arrhythmia from an ECG signal or a seizure from an EEG signal) through signal classification. Compared to conventional machine learning methods, neural-network-based AI classification methods are promising in achieving higher classification accuracy, but with significantly increased computational complexity, posing challenges to real-time performance and low power consumption. AI processors have been designed to accelerate neural networks for general AI applications such as image and voice recognition. They are not suitable for biomedical AI processing, which requires a combination of biomedical and AI processing hardware. In addition, the design redundancy for general AI applications results in large power consumption making it unsuitable for ultra-low-power health monitoring devices. There are also some biomedical AI processors such as ECG/EEG/EMG AI processors. However, they are customized for specific algorithms and tasks, prohibiting algorithm upgrades, limiting their applicability. In addition, prior designs lack adaptive learning to address the patient-to-patient variation issue. In this work, the BioAlP is proposed - a reconfigurable biomedical Al processor with adaptive learning. It has the following key features: 1) A reconfigurable biomedical Al processing architecture with reconfigurable neural network and biomedical processing engines to support versatile biomedical Al processing. 2) An event-driven biomedical Al processing architecture and approximate data compression technique to reduce power consumption. 3) An Al-based adaptive-learning architecture to address patient-to-patient variation. 4) A reconfigurable FIR engine reusing the neural-network engine to reduce the hardware overhead.

关键词： Adaptive learning Program processors Power demand Speech recognition artificial intelligence Task analysis Monitoring

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Efficient Detection and Categorization of Thyroid Nodules from Medical Ultrasound images

Efficient Detection and Categorization of Thyroid Nodules fr...

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artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International conference on

作者： Prarthana A. Deshkar Punit Fulzele Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur Datta Meghe Institute of Higher Education and Research Sawangi Wardha

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The use of ML and DL in medical imaging for early illness symptom prediction is substantial. Since 2013, DL has been one of the rising trends in general data analysis. With its multiple hidden layers that allow for a high degree of data abstraction, it is an enhancement of artificial neural networks (ANN). One promising approach for computer vision applications is the use of convolutional neural networks (CNNs). Automatic learning of raw data, particularly photographs, is a capability of deep CNNs. Both the capture and interpretation of images are crucial for the correct diagnosis or evaluation of illness. Devices are able to gather data at a rapid pace with improved resolution because to advancements in image acquisition over the last decade. Nevertheless, computer technology has only just started to improve picture interpretation. Since they are subjective and need highly trained doctors, they are often created by radiologist, physicians, and senior doctors. In medical imaging, computerised tools play a crucial role in facilitating results and improving diagnosis. Early detection and categorization of thyroid nodules by visual inspection and manual study of thyroid ultrasonography (USG) images has traditionally been a tedious process. Identifying benign from malignant thyroid nodules requires manual evaluation of thyroid USG images, which may be a time-consuming and laborious process. The medical industry has seen the emergence of many deep learning models, particularly for the categorization of thyroid nodules, thanks to the growth in processing resources and the fast improvement in technology. Clinical interventions and therapies may be more successful when these nodules are detected early. Thus, a growing number of academics are pushing for the use of computer diagnostic systems (CDS) as a means to statistically and scientifically evaluate USG pictures of thyroid nodules. Creating effective models for identifying and classifying thyroid nodules using ML and DL

关键词： Industries Deep learning Computational modeling Optimization methods Ultrasonography Manuals Convolutional neural networks Medical diagnostic imaging Thyroid Cancer

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Interpreting convolutional neural network by joint evaluation of multiple feature maps and an improved NSGA-ii algorithm

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EXPERT SYSTEMS WITH applications 2024年第PartA期255卷

作者： Wang, Zhenwu Zhou, Yang Han, Mengjie Guo, Yinan China Univ Min & Technol Sch Artificial Intelligence Beijing 100083 Peoples R China Dalarna Univ Sch Informat & Engn S-79188 Falun Sweden China Univ Min & Technol Dept Intelligent Control & Robot Beijing 100083 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China

The 'black box' characteristics of Convolutional neural networks (CNNs) present significant risks to their application scenarios, such as reliability, security, and division of responsibilities. Addressing the interpretability of CNN emerges as an urgent and critical issue in the field of machine learning. Recent research on CNN interpretability has either yielded unstable or inconsistent interpretations, or produced coarse-scale interpretable heatmaps, limiting their applicability in various scenarios. In this work, we propose a novel method of CNNs interpretation by incorporating a joint evaluation of multiple feature maps and employing multi-objective optimization (JE&MOO-CAM). Firstly, a method of joint evaluation for all feature maps is proposed to preserve the complete object instances and improve the overall activation values. Secondly, an interpretation method of CNNs under the MOO framework is proposed to avoid the instability and inconsistency of interpretation. Finally, the operators of selection, crossover, and mutation, along with the method of population initialization in NSGA-ii, are redesigned to properly express the characteristics of CNNs. The experimental results, including both qualitative and quantitative assessments along with a sanity check conducted on three classic CNN models-VGG16, AlexNet, and ResNet50-demonstrate the superior performance of the proposed JE&MOO-CAM model. This model not only accurately pinpoints the instances within the image requiring explanation but also preserves the integrity of these instances to the greatest extent possible. These capabilities signify that JE&MOO-CAM surpasses six other leading state-of-the-art methods across four established evaluation criteria.

关键词： Black box Convolutional neural network Interpretability Feature map Multi -objective optimization

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Automated CAPTCHA Generation from Annotated images Using Encoder Decoder Architecture

Automated CAPTCHA Generation from Annotated Images Using Enc...

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International conference on Information Technology (ICIT)

作者： Madhura Das Ashwin Naresh Anish Narang Anantharaman Narayana R. Jayashree Department of Computer Science PES Institute of Technology Bangalore India PES University Bangalore Karnataka IN

ISBN: (纸本)9781509035854

A CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a program that protects websites from bots by generating and grading assessments that humans can pass but current computer programs cannot. CAPTCHAs provide security from bots in applications such as preventing comment spam in blogs, protecting website registrations etc. Recent breakthroughs in artificial Intelligence (AI) have led to development of systems which can crack current image based CAPTCHAs with over 90% accuracy. This necessitates the need for new types of image based CAPTCHA which would be plausible for a human to solve it, and, at the same time, pose a challenge for a bot to break the CAPTCHA. To this end, the paper proposes a novel type of image based CAPTCHA where the user is presented with a set of images and a multiple answer based question based on the contents of the image-set. The question generated is such that a human is able to answer the question easily, whereas a bot would have to delve into the intricacies of image recognition, natural language processing on the question and then perform a knowledge correlation with the options to crack the CAPTCHA which is a rather tedious task to achieve. The novelty in the CAPTCHA presented can be expressed in terms of the CAPTCHA type itself as well as the deep learning architecture employed to synthesize the CAPTCHA. The proposed CAPTCHA generation system uses an Encoder Decoder architecture whose basic building block is a Gated Recurrent Unit (GRU) - a type of Recurrent neural Network (RNN). The proposed system also facilitates a dynamic CAPTCHA generation mechanism eliminating the need to store a mapping between the images and questions.

关键词： CAPTCHAs Decoding Computer architecture Recurrent neural networks Measurement Computers artificial intelligence

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Deep Learning Convolutional neural networks (CNNs) on Recognition and Classification of White Blood Cells (WBCs)

Deep Learning Convolutional Neural Networks (CNNs) on Recogn...

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Biomedical Engineering International conference (BMEiCON)

作者： Salila Ongtrakul Anyarin Thitirattanapong Anoma Eamkong Chuchart Pintavirooj Supan Tungjitkusolmun Department of Biomedical Engineering School of Engineering King Mongkut’s Institute of Technology Ladkrabang (KMITL) Bangkok Thailand

The rapid evolution of artificial Intelligence has brought significant advancements in Deep Learning, a widely applied subfield across various industries, including healthcare. This study focuses on leveraging Deep Learning and image processing techniques to classify white blood cells (WBCs). By comparing and evaluating multiple convolution neural network (CNN) models, such as GoogLeNet, ResNet50, VGG16, and Squeezenet, including YOLO (You Only Look Once) algorithm known for its segmentation capabilities, the objective is to improve the accuracy and efficiency of WBC recognition and classification. During training, both VGG16 and ResNet50 models achieved the highest accuracies, with VGG16 at 97.70% and ResNet50 at 97.38%. However, ResNet50 was chosen as the preferred model to be utilized alongside YOLO for improved classification and segmentation advancements. The testing phase of ResNet50 utilized 10% of the dataset from the initial data and demonstrated over 90% accuracy in each class. The process showed higher efficiency and lower capital costs, making it suitable for medical applications aided by Deep Learning Convolutional neural networks, addressing limitations in white blood cell classification.

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Improving Spatial Feature Representation from Aerial Scenes by Using Convolutional networks

Improving Spatial Feature Representation from Aerial Scenes ...

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Brazilian Symposium on Computer Graphics and image processing (SIBGRAPI)

作者： Keiller Nogueira Waner O. Miranda Jefersson A. Dos Santos Universidade Federal de Minas Gerais Belo Horizonte Brazil Dept. of Comput. Sci. Univ. Fed. de Minas Gerais Belo Horizonte Brazil

The performance of image classification is highly dependent on the quality of extracted features. Concerning high resolution remote image images, encoding the spatial features in an efficient and robust fashion is the key to generating discriminatory models to classify them. Even though many visual descriptors have been proposed or successfully used to encode spatial features of remote sensing images, some applications, using this sort of images, demand more specific description techniques. Deep Learning, an emergent machine learning approach based on neural networks, is capable of learning specific features and classifiers at the same time and adjust at each step, in real time, to better fit the need of each problem. For several task, such image classification, it has achieved very good results, mainly boosted by the feature learning performed which allows the method to extract specific and adaptable visual features depending on the data. In this paper, we propose a novel network capable of learning specific spatial features from remote sensing images, with any pre-processing step or descriptor evaluation, and classify them. Specifically, automatic feature learning task aims at discovering hierarchical structures from the raw data, leading to a more representative information. This task not only poses interesting challenges for existing vision and recognition algorithms, but also brings huge opportunities for urban planning, crop and forest management and climate modelling. The propose convolutional neural network has six layers: three convolutional, two fully-connected and one classifier layer. So, the five first layers are responsible to extract visual features while the last one is responsible to classify the images. We conducted a systematic evaluation of the proposed method using two datasets: (i) the popular aerial image dataset UCMerced Land-use and, (ii) a multispectral high-resolution scenes of the Brazilian Coffee Scenes. The experiments show that the pro

关键词： Feature extraction Neurons Remote sensing Mathematical model Biological neural networks Visualization Robustness

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