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Detection of Melanoma Insitu Using Trained CNN Model

SN computer Science 2024年第8期5卷 1-13页

作者： SethuMadhavi, R. Premkumar, Anitha Satheesha, T.Y. Bhasker, B. DharmaTheja, M. Asha, P.N. School of Computer Science Engineering REVA University Bengaluru 560064 India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education (MAHE) Karnataka Bangalore 560064 India School of Science and Technology Department of Engineering University of London London United Kingdom School of Computing and Information Technology REVA University Bengaluru 560064 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R & amp D Institute of Science and Technology Chennai India

Being the most deadly kind of skin cancer, melanoma presents a serious risk to life and is increasingly prevalent, particularly among men. It is characterized by aggressive cell multiplication that can spread within the body if left untreated, making early detection crucial for successful treatment. While machine learning techniques have been used for melanoma diagnosis, they often have limitations in performance. To address this, we propose an advanced deep learning framework incorporating a novel hybrid architecture that combines Convolutional Neural Networks (CNNs) with attention mechanisms. This framework also incorporates advanced techniques for model interpretability, such as visualizing the areas of the image that the model focuses on for its decision-making, to enhance transparency and trustworthiness for medical professionals and leverages transfer learning with pre-trained networks to boost performance. We utilized a range of optimization techniques, including AdaBelief and dynamic learning rates, to enhance model training. Our evaluation metrics included accuracy, sensitivity, specificity, and precision. Comparative results demonstrated that our model outperformed traditional techniques, with the ResNet50 architecture combined with the Adam optimizer achieving an accuracy of 92%, sensitivity of 90%, and specificity of 94%. These findings demonstrate the efficacy of our deep learning strategy by showing a notable improvement in melanoma detection performance. It is imperative to underscore that actual clinical trials or pilot studies are required to validate the model's effectiveness and reliability in real-world scenarios. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cancer detection Convolutional Neural Network (CNN) Deep learning Dermatology Melanoma Skin cancer detection Transfer learning

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Model-driven Time-varying Channel Estimation Method for OFDM System Based on Neural Network 24

Model-driven Time-varying Channel Estimation Method for OFDM...

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24th IEEE International Conference on Communication technology, ICCT 2024

作者： Zhu, Yue Fan, Dian Mu, Yunping Wang, Gongpu China Academy of Information and Communications Technology Beijing100191 China Beijing Jiaotong University Engineering Research Center of Network Management Technology for High Speed Railway Ministry of Education School of Computer and Information Technology Beijing100044 China

ISBN: (纸本)9798350363760

Channel estimation plays an important role in orthogonal frequency division multiplexing (OFDM) systems, which directly affects the performance of wireless communication systems in high-speed mobile scenarios. In this paper, we propose a model-driven channel estimation method for OFDM systems with doubly selective channels. Instead of completely abandoning traditional communication modules, we first obtain rough channel estimation using the least square (LS) method and then use a neural network called RefineNet for accuracy improvement. The results show that our proposed method significantly outperforms the linear minimum mean squared error (LMMSE) method. © 2024 IEEE.

关键词： Orthogonal frequency division multiplexing

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Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

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computer Systems Science & engineering 2023年第3期44卷 2243-2257页

作者： B.Kalpana S.Dhanasekaran T.Abirami Ashit Kumar Dutta Marwa Obayya Jaber S.Alzahrani Manar Ahmed Hamza Department of Information Technology RMD Engineering CollegeChennai601206India Department of Information Technology Kalasalingam Academy of Research and EducationSrivilliputtur626126India Department of Information Technology Kongu Engineering CollegeErode638060India Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Biomedical data classification has become a hot research topic in recent years,thanks to the latest technological advancements made in ***-dical data is usually examined by physicians for decision making process in patient *** manual diagnosis is a tedious and time consuming task,numerous automated models,using Artificial Intelligence(AI)techniques,have been presented so *** this motivation,the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI(BDC-CMBOAI)*** aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical ***,the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection(CMBO-FS)technique to derive a useful subset of *** addition,Ridge Regression(RR)model is also utilized as a classifier to identify the existence of *** novelty of the current work is its designing of CMBO-FS model for data ***,CMBO-FS technique is used to get rid of unwanted features and boosts the classification *** results of the experimental analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures.

关键词： Artificial intelligence biomedical data feature selection cat and mouse optimizer ridge regression

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Facial Emotion Recognition Method Based on Canny Edge Detection Using Convolutional Neural Network 4

Facial Emotion Recognition Method Based on Canny Edge Detect...

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Harshith, V. Shilpa, T. Siddappa, M. Banglore Institute of Technology Information Science and Engineering Department Bangalore India Sri Siddhartha Institute of Technology Computer Science and Engineering Department Tumkur India

ISBN: (纸本)9798350306118

Facial Emotion Recognition(FER) has made a pivotal contribution to Human Society by accomplishing complex tasks which humans struggle to achieve. The traditional Machine Learning (ML) models that follow explicit feature extraction from images require domain expertise, manual effort and also lack spatial invariance. The primary cause for the hindrance of the recognition accuracy is the imbalance of classes that exist in the datasets along with the limited availability of training data. To address these limitations, a facial emotion recognition method based on Canny edge detection utilizing a convolutional neural network is proposed in this paper. Important structural information from facial images is extracted to intensify the recognition rate, and techniques such as data augmentation, and oversampling are utilized. The proposed Convolutional Neural Network (CNN) model is trained using the dataset FER2013. The accuracy achieved with only 40 iterations of training was 76.92 percent with the devised method that surpasses other CNN architectural methods such as traditional CNN, Xception and AlexNet. © 2023 IEEE.

关键词： Emotion Recognition

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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Smoker Recognition from Lung X-ray Images using ML 26

Smoker Recognition from Lung X-ray Images using ML

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26th International Conference on computer and information technology, ICCIT 2023

作者： Chanda, Moumita Mazumder, Olive Karim Patwary, Md. Fazlul Jahangirnagar University Institute of Information Technology Savar1342 Bangladesh Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Smoking is the leading cause of death in Bangladesh, accounting for one in every five fatalities, according to scientific research. A number of researchers have developed cutting-edge techniques based on Deep Learning methods to ascertain a man's smoking status through image processing. As far as we are aware, no CNN-based system can distinguish between a smoker and a non-smoker from a lung X-ray image. In this work, we offer a novel CNN-based system that can detect, in real time, with high sensitivity and specificity, whether a man smokes or not by examining lung X-ray pictures. The employed data-set is divided into two groups: smokers and non-smokers. In this study, we provide a novel ML-based system that uses images of the guy's lungs taken in real time to detect, with high sensitivity and specificity, whether or not the man smokes. Hospitals may occasionally need to utilize this method to draw blood from a nonsmoker. Additionally, men who vape are not allowed to take college admissions exams or the army selection process. The efficacy of the proposed method for Smoker and Non-Smoker prediction was evaluated and contrasted with previous CNN systems based on multiple performance metrics. The proposed technique accurately detects smokers and nonsmokers from lung X-ray pictures with 91.50% accuracy, 92% precision, and 91% recall. We are certain that real-time system performance won't be affected, even if the recommended approach was trained on a picture dataset. This device might be used in a hospital, while choosing applicants for the police or army, or for university admission. © 2023 IEEE.

关键词： artificial intelligence CNN image augmentation InceptionMobileNetV2 non-smoker recognition smoker recognition X-ray Image detection

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Multiple moving object classification and tracking using DenCNN classifier

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期46卷 11311-11329页

作者： Premanand, V. Arulalan, V. Kumar, Dhananjay School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chengalpattu District Tamil Nadu Kattankulathur India Department of Information Technology Anna University Madras Institute of Technology Campus Tamil Nadu Chennai India

A Multiple moving object detection, tracking, and counting algorithm is mainly designed exclusively suitable for congested areas. The counting system can alleviate the betrayal performance in the crowded areas. Most of the existing methods developed for tracking and counting face serious challenges in detection due to high densities of the target. This condition urged the researchers to update the existing systems. The present methodology was designed to address such issues. In the present methodology, the contrast was initially enhanced between the objects and their backgrounds using a Double Plateau Histogram Equalization (DPHE). Then, the motion was estimated for the contrast-enhanced image to identify the moment of the object using the modified Adaptive Distance Covariance Rood Pattern Search (ADCRPS) algorithm. After that, the morphological operation was deployed to sharpen the images by removing all the unwanted things. Then, the features were extracted and important features were selected using the modified Chaotic Tent Shuffled Shepherd Optimization (CTSSO) Algorithm. With the selected features object, detection was done using the proposed Scaled Non-Monotonic Cauchy Dense Convolutional Neural Network (SNMC-DenCNN). The detected object was then tracked with the aid of Channel and Spatial Reliability Tracker (CSRT). Finally, the objects were counted by intersection over union (IOU) by explicitly computing the association between detected and tracked objects. Also, the experimental results showed the effectiveness and efficiency of the proposed system with enhanced accuracy. © 2024 - IOS Press. All rights reserved.

关键词： Convolution

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Effectiveness of Deep Learning Technologies to Assist Educational Inclusion for Children with Down Syndrome 5

Effectiveness of Deep Learning Technologies to Assist Educat...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Silva, Shehan Jayasinghe, Anjula Dilshan, Rishitha Dissanayake, Chandur Vidhanaarachchi, Samitha De Silva, Dilshan Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Down Syndrome (DS) is a genetic disorder causing intellectual disability and developmental delays. Despite instances of discrimination, several individuals with DS have achieved success through proper education and community support. This research addresses the challenges faced by children with DS in terms of motor skill development, cognitive impairments, and speech difficulties with the use of Deep Learning Technologies such as LSTM, CNN machine learning models. To bridge the educational gap and improve speech intelligibility, this study focuses on oral motor exercises that enhance articulation skills. Cognitive development in individuals with DS is also a concern, prompting the proposal of a self-learning system to foster cognitive growth. Fine motor skills improvement is explored through yoga exercises, and interventions like interactive technologies are considered to enhance learning experiences. This paper discusses the development of a user-friendly mobile application for early intervention in DS, integrating interactive activities using advanced technologies and a parent therapy dashboard. The system's potential for improving developmental skills and future expansion is emphasized. © 2023 IEEE.

关键词： Speech intelligibility

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Improving Data Science Automation via AI-Driven Systems 1

Improving Data Science Automation via AI-Driven Systems

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1st IEEE International Conference on Emerging Research in Computational Science, ICERCS 2023

作者： Swarup, Lakshya Pandey, Vijay Kumar Jayasree, S. More, Priyanka D. Das, Neeraj Haripriya, V. Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology India Vivekananda Global University Department of Mechanical Engineering Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai India Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350359756

a singular method to improve the effectiveness of information technological know-how automation approaches is proposed on this paper. This approach uses AI-pushed systems to force facts science automation. AI-pushed structures are used to appropriately extract insights from facts and locate the right paths to optimize various strategies related to information technological know-how automation. diverse case research were explored and effects are presented to demonstrate the effectiveness of this method, along with responsibilities including pre-processing statistics, creating suitable models, selecting appropriate algorithms, and so forth. Common, this technique has been proven to be powerful in enhancing records science automation tactics whilst simultaneously controlling expenses and reducing time-to-marketplace. © 2023 IEEE.

关键词： Automation

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Semar IoT Server Framework: Architecture and Technologies

Semar IoT Server Framework: Architecture and Technologies

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Menaka, C. Sahoo, Girija Shankar Sindgi, Anupama Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350357769

Modern Internet of Things, or IoT, devices are developing quickly, which has raised the possibility of creating smart communities where collaboration and the combination of diverse IoT apps are crucial. But standards for devices, logic, and data transfers are often overlooked in the design and deployment of IoT apps. In this study, we propose the creation and development of the SEMAR IoT server platform for standard-based IoT application platform integration. SEMAR provides Big Data settings with pre-built features for data synchronizations, aggregations, and predictive classifications. Additionally, plug-in features are simple to build. Representations Event Transfer Application Development Interface solutions may take data from devices for various sensors immediately and via networks, it will be utilized in immediate form for screens, text files, and for connecting to other systems. For SEMAR examinations, we set up the platform and incorporated five connected device systems: the indoor translation system built around fingerprints, the water quality surveillance framework, the ecological observing system as a whole and the air superior track system. With operates for managing IoT devices, data exchange, choices, synchronizations, and barriers that could be easily coupled with exterior programmed or applications for IoT with no modification to the coding, the recommended SEMAR IoT applications server platforms give higher flexibility and compatibility in comparison to prior research on IoT server platforms. The results support the suggestion's effectiveness and efficacy. © 2023 IEEE.

关键词： Air quality monitoring Big Data settings Data processing Data synchronization IoT application systems MQTT protocol Real-time data collection SEMAR IoT server platform Smart cities Water quality monitoring

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