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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer Science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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HANDS-ON LEARNING FOR ENHANCED THERMODYNAMICS EDUCATION WITH REFRIGERATION SYSTEM ANALYSIS

HANDS-ON LEARNING FOR ENHANCED THERMODYNAMICS EDUCATION WITH...

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ASME 2024 International Mechanical engineering Congress and Exposition, IMECE 2024

作者： Guo, Qi Everage, Daxton Zhang, Qinhe Department of Engineering & Computer Science McNeese State University Lake CharlesLA United States School of Mechanical Engineering Shandong University Jinan China

ISBN: (纸本)9780791888650

Thermodynamics is a fundamental subject in engineering education, yet it often poses challenges for students due to its abstract nature and complex concepts. One particularly difficult topic within thermodynamics is refrigeration cycles, which require a deep understanding of thermodynamic principles and their practical applications. This paper explores the implementation and impact of hands-on refrigeration labs in an engineering curriculum to enhance students' understanding of thermodynamics, specifically focusing on refrigeration systems. The hands-on labs utilize a refrigeration cycle hands-on trainer, providing students with practical experience in measuring temperature and pressure, analyzing heat transfer and thermodynamic work with enthalpy and entropy data, computing refrigerant mass flow rate and Coefficient of Performance (COP), and applying thermodynamic principles to real-world scenarios. By conducting these labs, students not only deepen their theoretical knowledge but also develop critical skills such as problem-solving, critical thinking, and practical application of engineering principles. The hands-on refrigeration labs demonstrate the effectiveness of experiential learning methodologies in enhancing students' understanding of thermodynamics. Through the lab activities, many students expressed that the labs significantly improved their understanding of refrigeration systems and their ability to apply thermodynamic concepts in engineering practice. This experiential learning approach bridges the gap between theory and practice, preparing students for future engineering challenges and enhancing their readiness for the workforce. Copyright © 2024 by ASME.

关键词： Students

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Player Performance Analysis Using Various Data Mining Approaches 2nd

Player Performance Analysis Using Various Data Mining Approa...

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2nd International Conference on Innovations in Data Analytics, ICIDA 2023

作者： Guruguri, Chandra Sekhar Reddy Pande, Sagar Dhanraj VIT-AP University Andhra Pradesh India Department of Computer Science and Engineering School of Engineering and Technology Pimpri Chinchwad University Maharashtra Pune India

ISBN: (纸本)9789819734658

The usage of machine learning in sports is increasing day by day. Cricket, as a sport centered around statistics and performance metrics, presents an abundant repository of data that holds the potential to reveal valuable insights into players’ skills and contributions during matches. Machine learning models analyze data produced from sports and provide game insights. Machine learning models are helpful in analyzing player performance and winner prediction. These models can also be used in players’ injury management and prevention. The data evaluated by machine learning models can guide the recruiters at the time of selecting the best team. The results of this study provide a sizable contribution to the body of information expanding on the revolutionary potential of machine learning in cricket. The idea behind this is to be able to forecast future events such as how many wickets a bowler will pick up and runs a batsman will accomplish. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sports

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Adaptive PV Systems: Using Genetic Algorithm and Random Forest Classifier to Mitigate the Effects of Partial Shading on Solar Photovoltaic Systems

Adaptive PV Systems: Using Genetic Algorithm and Random Fore...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Cabanayan, Ivan Perry M. Rodriguez, Adrian Enrico D. Peña, Robert Alfie S. MacAbebe, Erees Queen B. School of Science and Engineering Ateneo de Manila University Ateneo Energy Research Lab Department of Electronics Computer and Communications Engineering Quezon City Philippines

ISBN: (纸本)9798350350821

Mismatch losses due to partial shading of solar photovoltaic (PV) arrays can limit their generated power. One method to improve their power output is to electrically reconfigure the array based on the principle of row irradiance equalization. Shaded panels in an array are identified to determine the array's optimal configuration and maximize its power output. In this study, a random forest classification model was developed to identify the partial shading pattern incident on an array. Features of the model included the individual panel currents and the array irradiance of preset 20 shading patterns. The data used to train the model was obtained using an off-grid 3× 3 array composed of nine 10-W PV modules in total-cross-tied (TCT) topology. The model achieved a mean accuracy of 90% using the testing data and 98% using 5-fold cross-validation. This was integrated with a genetic algorithm-based irradiance equalization heuristic, which takes a predicted shading pattern as input and outputs an optimized configuration. For each shading pattern, the suggested reconfiguration was recorded and its corresponding power change was calculated. Power recovery ranged from -15.70% to 40%. The 3× 3 PV array reconfigured successfully to the optimum configuration. © 2024 IEEE.

关键词： Photovoltaics

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Detection and Classification of Human Emotion Using Deep Learning Model

Detection and Classification of Human Emotion Using Deep Lea...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Betageri, Deepa Yelamali, Vani School of Electronics and Communication Engineering KLE Technological University Hubli India Indian Institute of Information Technology Department of Computer Science & Engineering Dharwad India

ISBN: (纸本)9798331540685

Human emotions are the mental states resulting from neurophysiological changes that are diversely linked to human thoughts, feelings, and behavioral reactions. Facial expressions, eye movement and gestures are several nonverbal communication methods used in many human-machine interactions. Facial emotion recognition has been an essential field because it has many applications that convey the mental state and emotions of each individual. As there is no clear way to distinguish between emotions on a face and the complicated neural system that is connected to the face, recognizing human emotions is a difficult undertaking. In this paper, for students' facial expression recognition, we presented a model called Convolutional Neural Network (CNN). The proposed model consists of six convolutional layers, six max pooling with two fully connected layers. Using a Haar cascade detector, the system identifies the faces of students from input images. It categorizes them into seven different facial expressions, including surprised, fearful, disgusted, sadness, happiness, anger, and neutral. The FER2013 dataset yielded an accuracy rate of 77.22% for the proposed model. The comparative study is carried out with four other deep learning models. The models are Inception V3, VGG-16, VGG-19, Resnet50 and the accuracies achieved are 40%, 37%, 22.50%, 22.09%, respectively. © 2024 IEEE.

关键词： Contrastive Learning

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Optimization Management of Industrial Organizations Based on Performance Indicators

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World Journal of engineering and Technology 2024年第1期12卷 185-199页

作者： Anne Marie Chana Bernabé Batchakui Blaise Ndangang Department of Computer Engineering National Advanced School of Engineering Yaoundé Cameroon

This paper proposes an intelligent management system (IMS) to help managers in their delicate and tedious task of exploiting the plethora of data (indicators) contained in management dashboards. This system is based on intelligent agents, ontologies and data mining. It is implemented by PASSI (Process for Agent Societies Specification and Implementation) methods for agent design and implementation, the Methodology for Knowledge Modeling and Hot-Winters for data prediction. Intelligent agents not only track indicators but also store the knowledge of managers within the company. Ontologies are used to manage the representation and presentation aspects of knowledge. Data mining makes it possible to: make the most of all available data;model the industrial process of data selection, exploration and modeling;and transform behaviors into predictive indicators. An instance of the IMS named SYGISS, currently in operation within a large brewery organization, allows us to observe very interesting results: the extraction of indicators is done in less than 5 minutes whereas manual extraction used to take 14 days;the generation of dashboards is instantaneous whereas it used to take 12 hours;the interpretation of indicators is instantaneous whereas it used to take a day;forecasts are possible and are done in less than 5 minutes whereas they did not exist with the old management. These important contributions help to optimize the management of this organization.

关键词： Performance Indicator Intelligent Agent Data Mining Intelligent Management System Enterprise Management

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Push-Based Content Dissemination and Machine Learning-Oriented Illusion Attack Detection in Vehicular Named Data Networking

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computers, Materials & Continua 2023年第9期76卷 3131-3150页

作者： Arif Hussain Magsi Ghulam Muhammad Sajida Karim Saifullah Memon Zulfiqar Ali State Key Laboratory ofNetworking and Switching Technology Beijing University of Post and TelecommunicationBeijing100876China Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China School of Computer Science and Electronic Engineering University of EssexColchesterCO43SQUK

Recent advancements in the Vehicular Ad-hoc Network(VANET)have tremendously addressed road-related ***,Named Data Networking(NDN)in VANET has emerged as a vital technology due to its outstanding ***,the NDN communication framework fails to address two important *** current NDN employs a pull-based content retrieval network,which is inefficient in disseminating crucial content in Vehicular Named Data Networking(VNDN).Additionally,VNDN is vulnerable to illusion attackers due to the administrative-less network of autonomous *** various solutions have been proposed for detecting vehicles’behavior,they inadequately addressed the challenges specific to *** deal with these two issues,we propose a novel push-based crucial content dissemination scheme that extends the scope of VNDN from pullbased content retrieval to a push-based content forwarding *** addition,we exploitMachine Learning(ML)techniques within VNDN to detect the behavior of vehicles and classify them as attackers or *** trained and tested our system on the publicly accessible dataset Vehicular Reference Misbehavior(VeReMi).We employed fiveML classification algorithms and constructed the bestmodel for illusion attack *** results indicate that RandomForest(RF)achieved excellent accuracy in detecting all illusion attack types in VeReMi,with an accuracy rate of 100%for type 1 and type 2,96%for type 4 and type 16,and 95%for type ***,RF can effectively evaluate the behavior of vehicles and identify attacker vehicles with high *** ultimate goal of our research is to improve content exchange and ***,ourML-based attack detection and preventionmechanismensures trustworthy content dissemination and prevents attacker vehicles from sharing misleading information in VNDN.

关键词： Named data networking vehicular networks pull-push illusion attack machine learning

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Interactive Multimodal Learning: Towards Using Pedagogical Agents for Inclusive Education

Interactive Multimodal Learning: Towards Using Pedagogical A...

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2023 IEEE International Humanitarian Technology Conference, IHTC 2023

作者： Al Omoush, Muhammad H. Salih, Sumaia E. Kishore, Sameer Mehigan, Tracey Dublin City University Faculty of Engineering and Computing Dublin Ireland Dublin Ireland Middlesex University Department of Computer Engineering and Informatics Dubai United Arab Emirates

ISBN: (纸本)9798350314311

A pedagogical agent is an animated interface in an interactive online learning environment. Its role involves guiding users through instructions and participating in direct discussions. Numerous research studies underline the advantages of pedagogical agents in delivering instruction, tailoring learning experiences, and promoting inclusivity. The authors utilize machine learning techniques to develop an interactive multimodal application (combining visuals and audio) designed as a pedagogical agent. This application is intended to assist in teaching primary school students (aged 6 to 8) the recognition of colors and letters. Additionally, it incorporates voice interaction to assess their learning progress. The overarching objective of this pedagogical agent is to seamlessly integrate artificial intelligence (AI) into educational settings, thereby amplifying student engagement and motivation during the learning process. This paper will explore the journey of creating a machine learning-based application and its potential utility in supporting educators. Furthermore, the authors will investigate potential augmentations that could render the application more adept at aiding students with dyslexia. The paper will emphasize the significance of human-computer interaction (HCI), as it significantly shapes pedagogical agents' design and operational aspects. © 2023 IEEE.

关键词： Graphical user interfaces

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Detection of False Information in Digital Media Using Blockchain Technology 3

Detection of False Information in Digital Media Using Blockc...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Kilaru, Aswini Prasad, L V Narasimha Ghantasala, G S Pradeep Ananthanagu, U. Kallam, Suresh Rajesh Sharma, R. Institute of Aeronautical Engineering Department of Cse Telangana Hyderabad India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bengaluru India Center of Excellence in Immersive Technologies Alliance University Bengaluru India School of Computer Science and Engineering Jain Deemed to Be University Bengaluru India

ISBN: (纸本)9798350354379

The spreading of false information over the internet, highly impacts society and causes social and economic loss to the nation. One of the most important factors for the increase in the number of misleading information is that all the users of any particular digital platform usually do not verify the authenticity of the information. The principal impartial of this proposed work is to afford a solution to society to reduce the social and economic losses caused by spreading false information circulated digitally. Blockchain is a disseminated, decentralized network that conducts peer-to-peer transactions, meaning that it does not have a central entity to operate. Here we present a literature survey on various approaches towards this problem statement and investigate a Blockchain-based solution that focuses on all types of digital content by providing the end user to validate the information along with the evidence for data that was collected at each step. © 2024 IEEE.

关键词： Distributed ledger

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Classification of Fraud Websites using Linear regression Algorithm and Recurrent Neural Network Algorithm with Improved Accuracy

Classification of Fraud Websites using Linear regression Alg...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Roshan, M D.Raaquib Loganayagi, S. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350309140

The purpose of this work is to improve the detection of fraud websites using Novel Linear Regression Algorithm and Recurrent Neural Network Algorithm. Materials and Methods: Novel Linear regression Algorithm and Recurrent Neural Network Algorithm is executed with varying training and testing splits for predicting the detection of fraud websites. This research utilizes Novel Linear regression Algorithm and Recurrent Neural Network Algorithm with various grounding categories to predict the detection of extremist reviews. The data set in this article utilizes kaggle website data csv file repositories and number of samples (N=11053) The Gpower test used is about 85% (g power setting parameters: α=0.05 and power=0.85). Results: Novel Linear regression Algorithm (98.15%) has the increased accuracy over Recurrent Neural Network Algorithm (97.22%) with an independent samples-t-test significance value of p=0.01 (p © 2024 IEEE.

关键词： Deception Fraud Detection Novel Linear regression Algorithm Recurrent Neural Network Algorithm Social Protection Swindling

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