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Optimization of machine learning and Deep learning Algorithms for Diagnosis of Cancer 1

Optimization of Machine Learning and Deep Learning Algorithm...

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1st international conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Krishna, Hari Anand, M. Saravanan, D. Pushpalatha, K. Department of ECE B V Raju Institute of Technology Telangana India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of CSE IFET College of Engineering Villupuram India Department of Mathematics KoneruLakshmaiah Educational Foundation Vadedeswaram India

ISBN: (纸本)9781607685395

machine learning and artificial intelligence has recently become a prominent technology. Given its popularity and strength in pattern recognition and categorization, many corporations and institutions have begun investing in healthcare research to improve illness prediction accuracy. Using these strategies, however, has several drawbacks. One of the primary issues is the lack of huge data sets for medical pictures. An introduction to deep learning in medical image processing, from theoretical foundations to real-world applications. The article examines the general appeal of deep learning (DL), a collection of computer science advances. The next step was to learn the basics of neural networks. That explains the use of deep learning and CNNs. So we can see why deep learning is rapidly advancing in various application fields, including medical image processing. The goal of this research was to use innovative methodologies on cancer datasets to explore the feasibility of combining machine learning and deep learning algorithms for cancer detection. This study used text and picture databases to classify cancer. The datasets are the Liver BUPA disorder database and brain MRI pictures. This article provides optimization methods that outperform the suggested approaches' accuracy. Using two alternative training methods, Levenberg Marquardt (lm) and Resilient back propagation (rp), two classification algorithms were evaluated with different groups of neurons to identify benign and malignant patients. Cascade correlation utilizing the train (rp) outperformed feed forward back propagation using the train (lm). The second deep neural network model presented a technique (based on CNN) for automated brain tumour identification using MRI data. The Water Cycle Algorithm is used to optimise CNN. The established approach is very accurate. The suggested framework examines innovative texture classification algorithms using Discrete Wavelet Transform (DWT) and the Gray Level Co occurrence

关键词： Artificial Neural Network Batch Normalization Deep learning Gray Level Co-occurrence Matrix Water Cycle Algorithm

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Experimental Results on Multi-modal Deepfake Detection 21st

Experimental Results on Multi-modal Deepfake Detection

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21st international conference on image Analysis and processing (ICIAP)

作者： Concas, Sara Gao, Jie Cuccu, Carlo Orru, Giulia Feng, Xiaoyi Marcialis, Gian Luca Puglisi, Giovanni Roli, Fabio Univ Cagliari Cagliari Italy Northwestern Polytech Univ Xian Peoples R China Univ Genoa Genoa Italy

ISBN: (纸本)9783031064302;9783031064296

The advantages of deepfakes in many applications are counterbalanced by their malicious use, for example, in reply-attacks against a biometric system, identification evasion, and people harassment, when they are widespread in social networks and chatting platforms (cyberbullying) as recently documented in newspapers. Due to its "arms-race" nature, deepfake detection systems are often trained on a certain class of deepfakes and showed their limits on never-seen-before classes. In order to shed some light on this problem, we explore the benefits of a multimodal deepfake detection system. We adopted simple fusion rules, which showed their effectiveness in many applications, for example, biometric recognition, to exploit the complementary of different individual classifiers, and derive some possible guidelines for the designer.

关键词： Deepfake pattern recognition Multiple classifiers

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A Novel Method for Improving the Voxel-pattern-Based Euler Number Computing Algorithm of 3D Binary images 21st

A Novel Method for Improving the Voxel-Pattern-Based Euler N...

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21st international conference on image Analysis and processing (ICIAP)

作者： Yao, Bin Han, Dianzhi Kang, Shiying Chao, Yuyan He, Lifeng Shaanxi Univ Sci & Technol Artificial Intelligence Inst Sch Elect Informat & Artificial Intelligence Xian 710021 Shaanxi Peoples R China Xianyang Normal Univ Sch Comp Sci Xianyang 712000 Shaanxi Peoples R China Nagoya Sangyo Univ Fac Environm Informat & Business Nagoya Aichi 4888711 Japan Aichi Prefectural Univ Fac Informat Sci & Technol Nagoya Aichi 4801198 Japan

ISBN: (纸本)9783031133244;9783031133237

As an important topological property of a 3D binary image, the Euler number can be calculated by counting certain 2 x 2 x 2 voxel patterns in the image. This paper presents a novel method for improving the voxel-pattern-based Euler number computing algorithm of 3D binary images. In the proposed method, by changing the accessing order of voxels in 2 x 2 x 2 voxel patterns and combining the voxel patterns which provide the same Euler number increments for the given image, the average numbers of voxels to be accessed for processing a 2 x 2 x 2 voxel pattern can be decreased from 8 to 4.25, which will lead to an efficient processing. Experimental results demonstrated that the proposed method is much more efficient than the conventional voxel-pattern-based Euler number computing algorithm.

关键词： Euler number Topological property 3D image Computer vision pattern recognition

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A Review Paper on Automatic Number Plate recognition using machine learning : An In-Depth Analysis of machine learning Techniques in Automatic Number Plate recognition: Opportunities and Limitations 1

A Review Paper on Automatic Number Plate Recognition using M...

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1st international conference on Computational Intelligence, Communication Technology and Networking, CICTN 2023

作者： Prajapati, Rajat Kumar Bhardwaj, Yatharth Jain, Ritesh Kumar Kamal Kant Hiran, Dr. Geetanjali Institute of Technical Studies Department of Computer Science & Engineering Rajasthan Udaipur India Geetanjali Institute of Technical Studies Cse Rajasthan Udaipur India Symbiosis University of Applied Sciences Cse Madhya Pradesh Indore India

ISBN: (纸本)9798350338027

The recognition of license plates, known as Automatic Number Plate recognition (ANPR), is an important topic in the fields of smart transportation systems and image recognition. ANPR has the potential to significantly improve traffic control and safety measures in urban areas. Its primary objective is to automatically detect and recognize the characters on vehicle license plates using modern technology and image processing techniques. With the rapid increase in the number of vehicles, ANPR has become a crucial tool for traffic control and management. In recent years, ANPR technology has advanced significantly, leading to the development of protocols such as OCR, which enables the recognition of license plate locations and allows traffic surveillance to address various issues, such as criminal investigations, toll collection, speed control, and parking management. ANPR systems play a vital role in traffic control and surveillance, and several systems have been developed to recognize license plates, each with its specific advantages. The key steps involved in an ANPR system are the precise localization of the number plate, segmentation, and recognition. This paper presents an efficient and layout-independent ANPR system that can accurately detect and recognize license plates, thus enhancing traffic control and surveillance measures. © 2023 IEEE.

关键词： Object detection

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Blind Deblurring of Hyperspectral Document images 21st

Blind Deblurring of Hyperspectral Document Images

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21st international conference on image Analysis and processing (ICIAP)

作者： Ljubenovic, Marina Guzzonato, Paolo Franceschin, Giulia Traviglia, Arianna Ist Italiano Tecnol Ctr Cultural Heritage Technol Venice Italy Ca Foscari Univ Venice Venice Italy

ISBN: (纸本)9783031133213;9783031133206

Most computer vision and machine learning-based approaches for historical document analysis are tailored to grayscale or RGB images and thus, mostly exploit their spatial information. Multi-spectral (MS) and hyperspectral (HS) images contain, next to the spatial information, a much richer spectral information than RGB images (usually spreading beyond the visible spectral range) that can facilitate more effective feature extraction, more accurate classification and recognition, and thus, improved analysis. Although utilization of a rich spectral information can improve historical document analysis tremendously, there are still some potential limitations of HS imagery such as camera induced noise and blur that require a carefully designed preprocessing step. Here, we propose a novel blind HS image deblurring method tailored to document images. We exploit a low-rank property of HS images (i.e., by projecting a HS image to a lower dimensional subspace) and utilize a text tailor image prior to performing a PSF estimation and deblurring of subspace components. The preliminary results show that the proposed approach gives good results over all spectral bands, removing successfully image artefacts introduced by blur and noise and significantly increasing the number of bands that can be used in further analysis.

关键词： Document images Hyperspectral image processing Deblurring

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A Systematic Review on Screening of Diabetic Retinopathy and Maculopathy Using Artificial Intelligence 1st

A Systematic Review on Screening of Diabetic Retinopathy and...

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1st international conference on Advances in Signal processing and Communication Engineering (ICASPACE)

作者： Jones, Aida Vijayan, Thulasi Bai Subbarayan, Sadasivam John, Sheila KCG Coll Technol Dept Elect & Commun Engn Chennai 600097 Tamil Nadu India Sankara Nethralaya Eye Hosp Dept Teleophthalmol Chennai 600006 Tamil Nadu India

ISBN: (纸本)9789811955525;9789811955501;9789811955495

Diabetes mellitus is a condition that occurs when the glucose level in the blood goes high. The eye-related issues due to diabetes may include diabetic retinopathy and diabetic maculopathy. It also includes conditions such as Glaucoma and Cataracts. All these conditions can steer toward poor vision and blindness. Artificial intelligence (AI) is the latest methodology used for eye image analysis. It is all about intelligent programming with the help of intelligent algorithms that make intelligent machines do what a human does. machine learning and deep learning techniques are subtypes of AI. In this paper, we have summarized the findings of studies that have detected diabetic retinopathy and diabetic maculopathy using various AI methods.

关键词： Diabetic retinopathy (DR) Diabetic maculopathy (DM) Diabetic macular edema (DME) Fundus images Teleophthalmology machine learning (ML) Deep learning (DL)

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A Convolutional Neural Network-Based Web Prototype to Support Melanoma Skin Cancer Detection 3

A Convolutional Neural Network-Based Web Prototype to Suppor...

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3rd international conference on Information Systems and Software Technologies (ICI2st)

作者： Rosas-Lara, Mauro Mendoza-Tello, Julio C. Flores, Aldrin Zumba-Acosta, Gema Univ Cent Ecuador Fac Ingn & Ciencias Aplicadas Quito Ecuador

ISBN: (纸本)9781665455176

The skin is the organ that protects the human body. However, factors such as solar radiation damage the texture and skin cells. Sometimes, the lack of timely diagnosis leads to skin cancer. In this line, melanoma is the most dangerous type of cancer and has caused the greatest number of deaths related to skin diseases. With this problem, collaborative efforts between different research areas are necessary to support early detection of the disease. In this way, the evolution of algorithms based on neural networks plays an important role for image processing, which is an essential activity for pattern detection and recognition in medical diagnosis. Faced with this challenge, this research proposes a web prototype based on convolutional neural networks to support melanoma detection. In this context, the reference framework suggested by the Cross Industry standard Process for Data Mining (CRISP-DM) was used. For this, 18,000 high-quality images were compiled from the data science community. In addition, two learning models (based on convolutional neural network and Res-Net50) were created and evaluated. With these premises, a web application was developed using the waterfall model. Finally, conclusions and future work are suggested at the end of the document.

关键词： machine learning neural network melanoma data mining skin cancer

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A Portable Object Detection System for Visually Impaired Individuals in Outdoor Environments

A Portable Object Detection System for Visually Impaired Ind...

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Advanced Computing and Emerging Technologies (ACET), international conference on

作者： Prabhat Kr Srivastava Vibhor Harit Shashank Kumar Mall BasuDeo Singh Roohani Tushar Pandey Yash Goel Department of Computer Science and Engineering IMS Engineering College Ghaziabad Uttar Pradesh

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

In recent years, progress in machine learning methods has greatly influenced the creation of assistive technologies designed to enhance the quality of life for individuals with visual impairments. This paper introduces a unique real-time image analysis method specifically developed for the visually impaired community. Leveraging the You Only Look Once (YOLO) dataset and machine learning algorithms implemented in Python, our The suggested system provides a seamless and efficient solution for identifying objects in the circumventing environment in genuine-time. In this study, we performed comprehensive experiments to assess the effectiveness of our system, taking into account aspects like detection accuracy, processing speed, and usability for users with visual impairments. The results demonstrate the efficacy and reliability of our approach in genuine-world scenarios, showcasing its potential to be accommodated as a valuable and implement for improving autonomy and mobility of visually impaired individuals.

关键词： YOLO machine learning algorithms Accuracy Visual impairment machine learning Assistive technologies Real-time systems Reliability Object recognition Usability

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2022 IEEE 1st international conference on Internet of Digital Reality, IoD 2022

2022 IEEE 1st International Conference on Internet of Digita...

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1st IEEE international conference on Internet of Digital Reality, IoD 2022

ISBN: (纸本)9781665499651

The proceedings contain 17 papers. The topics discussed include: predicting the impact of type changes on overall equipment effectiveness (OEE) through machine learning;technical feasibility and design challenges of unmanned aerial vehicle based drive testing on cellular networks;tensor-based format for exchanging hypergraphs between cognitive entities;recommendations on electromagnetic compatibility testing of unmanned aerial vehicles;a mobile application for training echolocation with a novel method for spatially rendered echoes;outlines of a graph-tensor based adaptive associative search model for Internet of digital reality applications;accessibility evaluation of healthcare webpages in Hungary using accessibility barrier computation algorithm;handheld 3D scanning and image processing for printing body parts - a workflow concept and current results;and defining synergies between robotics, cognitive infocommunications and internet of digital reality.

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ISAApp – image Based Smart Attendance Application 1st

ISAApp – Image Based Smart Attendance Application

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1st international conference on Advances in Artificial Intelligence and machine learning in Big Data processing, AAIMB 2023

作者： Dutta, Aritra Suseela, G. Niranjana, G. Boral, Pushpita Gupta, Pranav Pal, Subha Bal Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9783031730641

This research paper introduces a Python-based implementation of a facial recognition system utilizing face recognition and Open CV libraries. The system has diverse applications, including security, surveillance, social media, and entertainment. By employing a pre-trained neural network, the system enables users to select an image file and accurately detect and recognize faces within it. Additionally, it includes functionality to encode faces from a specified folder, enabling comparison against the faces in the target image. By employing a blend of face detection, feature extraction, and machine learning algorithms, the system effectively identifies, and labels faces in images. Its effectiveness illustrates the potential of facial recognition technology for numerous applications, while also underscoring the ethical and privacy concerns associated with such technology. Addressing these issues and developing more precise and dependable systems would require further research. The primary objective of the system is to offer an improved solution for classroom attendance, ensuring the avoidance of false attendance records and significantly reducing the time required for attendance taking, through the application of machine learning solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Support vector machines

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